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Becoming 1% better at data science everyday

learning

Learning Philosophy:

• The Power of Tiny Gains
• Master Adjacent Disciplines
• T-shaped skills
• Data Scientists Should Be More End-to-End

Develop a business acumen

• [X] Book: Delivering Happiness
• [X] Book: Good to Great: Why Some Companies Make the Leap...And Others Don't
• [X] Book: Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams
• [X] Book: How Google Works
• [X] Book: Learn to Earn: A Beginner's Guide to the Basics of Investing and Business
• [X] Book: Rework
• [X] Book: The Airbnb Story
• [X] Book: The Personal MBA
• [X] Facebook: Digital marketing: get started
• [X] Facebook: Digital marketing: go further
• [X] Google Analytics for Beginners
• [ ] Google: Fundamentals of Digital Marketing
• [X] Moz: The Beginner's Guide to SEO
• [X] Smartly: Marketing Fundamentals
• [X] Treehouse: SEO Basics
• [ ] Udacity: App Monetization
• [ ] Udacity: App Marketing
• [ ] Udacity: Get Your Startup Started
• [ ] Udacity: How to Build a Startup
• [ ] Youtube: SEO Unlocked
  • [X] Welcome to the SEO Unlocked 0:10:09
  • [X] Introduction to SEO and Why It's Important 0:10:29
  • [ ] Keyword Research Part 1 0:19:20
  • [ ] Keyword Research Part 2 0:09:56
  • [ ] On-page and technical SEO Part 1 0:22:58
  • [ ] On-page and technical SEO Part 2 0:12:16
  • [ ] Mastering Technical SEO Audits 0:16:35
  • [ ] Content Marketing Part 1 0:24:09
  • [ ] Advanced Content Marketing Tactics 0:09:54
  • [ ] The 10 Commandments of Content Marketing 0:19:01
  • [ ] How to Edit Your Content For SEO 0:10:59
  • [ ] Discover Your Competitive Strategy 0:09:12
  • [ ] Over 4 Million Backlinks Built With This Simple Process 0:11:09
  • [ ] How to Get POWERFUL Backlinks for Faster Rankings 0:09:40
  • [ ] Get THOUSANDS of Backlinks On Semi-Autopilot 0:06:32
  • [ ] How To Get The Most Out Of Google Analytics 0:07:45
  • [ ] How to Setup Google Search Console 0:09:21
  • [ ] How to Use Advanced Features in Google Analytics 0:10:52
  • [ ] A Deep Dive Into Branding, Data & Experience 0:14:03
  • [ ] How To Create A Compelling Brand 0:05:52
  • [ ] Designing Your Customer Experience & Case Studies 0:07:32
• [ ] Youtube: Webinars From The Future | Session One: Design Thinking
• [ ] Youtube: Webinars From The Future | Session Two: Interaction Design

Be able to frame a ML problem

• [X] AWS: Types of Machine Learning Solutions
• [X] Article: Apply Machine Learning to your Business
• [X] Book: AI Superpowers: China, Silicon Valley, and the New World Order
• [X] Book: A Human's Guide to Machine Intelligence
• [X] Book: The Future Computed
• [ ] Book: Machine Learning Yearning by Andrew Ng
• [X] Book: Prediction Machines: The Simple Economics of Artificial Intelligence
• [ ] Book: Building Machine Learning Powered Applications: Going from Idea to Product
• [X] Coursera: AI For Everyone
• [ ] Datacamp: Case Studies in Statistical Thinking
• [X] Datacamp: Data Science for Everyone
• [X] Datacamp: Machine Learning with the Experts: School Budgets
• [X] Datacamp: Machine Learning for Everyone
• [ ] Datacamp: Analyzing Police Activity with pandas
• [X] Datacamp: Data Science for Managers
• [X] Facebook: Field Guide to Machine Learning
• [ ] Google: Art and Science of Machine Learning
• [ ] Google: How Google does Machine Learning
• [X] Google: Introduction to Machine Learning Problem Framing
• [ ] Microsoft: Define an AI strategy to create business value
• [ ] Microsoft: Discover ways to foster an AI-ready culture in your business
• [ ] Microsoft: Identify guiding principles for responsible AI in your business
• [ ] Microsoft: Introduction to AI technology for business leaders
• [X] Pluralsight: How to Think About Machine Learning Algorithms
• [ ] Udacity: Problem Solving with Advanced Analytics
• [X] Youtube: Vincent Warmerdam: The profession of solving (the wrong problem) | PyData Amsterdam 2019
• [X] Youtube: Making Money from AI by Predicting Sales - Jay's Intro to AI Part 2
• [X] Youtube: How does YouTube recommend videos? - AI EXPLAINED! 0:33:53
• [X] Youtube: How does Google Translate's AI work? 0:15:02
• [X] Youtube: Data Science in Finance 0:17:52
• [X] Youtube: The Age of AI
  • [X] How Far is Too Far? | The Age of A.I. 0:34:39
  • [X] Healed through A.I. | The Age of A.I. 0:39:55
  • [X] Using A.I. to build a better human | The Age of A.I. 0:44:27
  • [X] Love, art and stories: decoded | The Age of A.I. 0:38:57
  • [X] The 'Space Architects' of Mars | The Age of A.I. 0:30:10
  • [X] Will a robot take my job? | The Age of A.I. 0:36:14
  • [X] Saving the world one algorithm at a time | The Age of A.I. 0:46:37
  • [X] How A.I. is searching for Aliens | The Age of A.I. 0:36:12
• [ ] Youtube: Gradient Dissent Podcast
  • [ ] DeepChem creator Bharath Ramsundar on using deep learning for molecules and medicine discovery 0:55:11
  • [X] ML Research and Production Pipelines with Chip Huyen 0:43:07
  • [ ] Product Management for AI with Peter Skomoroch 1:28:14
  • [X] Slow down and change one thing at a time - Advancing AI research with Josh Tobin 0:48:19
  • [ ] Societal Impacts of Artificial Intelligence with Miles Brundage 1:02:25
  • [ ] Deep Reinforcement Learning and Robotics with Peter Welinder 0:54:22
  • [X] Machine learning across industries with Vicki Boykis 0:34:02
  • [ ] Designing ML models for millions of consumer robots - Angela Bassa and Danielle Dean 0:52:38
  • [ ] Building trustworthy AI systems and combating potential malicious use – A conversation w/ Jack Clark 0:55:56
  • [X] Rachael Tatman - Conversational A.I. and Linguistics 0:36:51
  • [ ] Nicolas Koumchatzky - Machine Learning in Production for Self Driving Cars 0:44:56
  • [ ] Brandon Rohrer - Machine Learning in Production for Robots 0:34:31

Understand data ethics better

• [ ] Practical Data Ethics
  • [ ] Lesson 1: Disinformation
  • [ ] Lesson 2: Bias & Fairness
  • [ ] Lesson 3: Ethical Foundations & Practical Tools
  • [ ] Lesson 4: Privacy and surveillance
  • [ ] Lesson 4 continued: Privacy and surveillance
  • [ ] Lesson 5.1: The problem with metrics
  • [ ] Lesson 5.2: Our Ecosystem, Venture Capital, & Hypergrowth
  • [ ] Lesson 5.3: Losing the Forest for the Trees, guest lecture by Ali Alkhatib
  • [ ] Lesson 6: Algorithmic Colonialism, and Next Steps

Be able to annotate data efficiently

• [X] Youtube: Snorkel: Dark Data and Machine Learning - Christopher Ré
• [X] Youtube: Training a NER Model with Prodigy and Transfer Learning
• [X] Youtube: Training a New Entity Type with Prodigy – annotation powered by active learning

Be able to manipulate data with Numpy

• [X] Datacamp: Intro to Python for Data Science
• [X] Pluralsight: Working with Multidimensional Data Using NumPy

Be able to manipulate data with Pandas

• [X] Datacamp: pandas Foundations
• [ ] Datacamp: Pandas Joins for Spreadsheet Users
• [X] Datacamp: Manipulating DataFrames with pandas
• [ ] Datacamp: Merging DataFrames with pandas
• [ ] Datacamp: Data Manipulation with pandas
• [ ] Datacamp: Optimizing Python Code with pandas
• [ ] Datacamp: Streamlined Data Ingestion with pandas
• [ ] Datacamp: Analyzing Marketing Campaigns with pandas
• [ ] Article: Modern Pandas
  • [ ] Modern Pandas (Part 1)
  • [ ] Modern Pandas (Part 2)
  • [ ] Modern Pandas (Part 3)
  • [ ] Modern Pandas (Part 4)
  • [ ] Modern Pandas (Part 5)
  • [ ] Modern Pandas (Part 6)
  • [ ] Modern Pandas (Part 7)
  • [ ] Modern Pandas (Part 8)

Be able to manipulate data in spreadsheets

• [X] Datacamp: Spreadsheet basics
• [ ] Datacamp: Data Analysis with Spreadsheets
• [ ] Datacamp: Intermediate Spreadsheets for Data Science
• [ ] Datacamp: Pivot Tables with Spreadsheets
• [ ] Datacamp: Data Visualization in Spreadsheets
• [ ] Datacamp: Introduction to Statistics in Spreadsheets
• [ ] Datacamp: Conditional Formatting in Spreadsheets
• [ ] Datacamp: Marketing Analytics in Spreadsheets
• [ ] Datacamp: Error and Uncertainty in Spreadsheets
• [X] edX: Analyzing and Visualizing Data with Excel

Be able to manipulate data in databases

• [X] Codecademy: SQL Track
• [X] Datacamp: Intro to SQL for Data Science
• [ ] Datacamp: Introduction to MongoDB in Python
• [ ] Datacamp: Intermediate SQL
• [ ] Datacamp: Exploratory Data Analysis in SQL
• [ ] Datacamp: Joining Data in PostgreSQL
• [X] Datacamp: Querying with TransactSQL
• [ ] Datacamp: Introduction to Databases in Python
• [ ] Datacamp: Reporting in SQL
• [ ] Datacamp: Applying SQL to Real-World Problems
• [ ] Datacamp: Analyzing Business Data in SQL
• [ ] Datacamp: Data-Driven Decision Making in SQL
• [ ] Datacamp: Database Design
• [ ] Udacity: SQL for Data Analysis
• [ ] Udacity: Intro to relational database
• [ ] Udacity: Database Systems Concepts & Design

Be able to use the command line

• [X] Codecademy: Learn the Command Line
• [X] Datacamp: Introduction to Shell for Data Science
• [ ] Datacamp: Data Processing in Shell
• [ ] LaunchSchool: Introduction to Commandline
• [ ] Learn Enough Command Line to be dangerous
• [ ] Thoughtbot: Mastering the Shell
• [ ] Thoughtbot: tmux
• [X] Udacity: Linux Command Line Basics
• [ ] Udacity: Linux Web Servers
• [X] Udacity: Shell Workshop
• [ ] Udacity: Web Tooling & Automation
• [ ] Web Bos: Command Line Power User

Be able to import data from multiple sources

• [X] Datacamp: Importing Data in Python (Part 2)
• [ ] Datacamp: Web Scraping in Python

Be able to perform feature engineering

• [ ] Article: Preparing data for a machine learning model
• [ ] Article: Feature selection for a machine learning model
• [ ] Article: Learning from imbalanced data
• [ ] Article: Hacker's Guide to Data Preparation for Machine Learning
• [ ] Article: Practical Guide to Handling Imbalanced Datasets
• [ ] Datacamp: Analyzing Social Media Data in Python
• [X] Datacamp: Dimensionality Reduction in Python
• [X] Datacamp: Preprocessing for Machine Learning in Python
• [X] Datacamp: Data Types for Data Science
• [X] Datacamp: Cleaning Data in Python
• [X] Datacamp: Feature Engineering for Machine Learning in Python
• [ ] Datacamp: Importing & Managing Financial Data in Python
• [ ] Datacamp: Manipulating Time Series Data in Python
• [ ] Datacamp: Working with Geospatial Data in Python
• [ ] Datacamp: Analyzing IoT Data in Python
• [ ] Datacamp: Dealing with Missing Data in Python
• [ ] Datacamp: Exploratory Data Analysis in Python
• [X] edX: Data Science Essentials
• [ ] Google: Feature Engineering
• [ ] Udacity: Creating an Analytical Dataset

Be able to experiment in notebook

• [X] Pluralsight: Getting Started with Jupyter Notebook and Python

Be able to visualize data

• [ ] Datacamp: Introduction to Data Visualization with Python
• [X] Datacamp: Introduction to Seaborn
• [X] Datacamp: Introduction to Matplotlib
• [ ] Datacamp: Intermediate Data Visualization with Seaborn
• [ ] Datacamp: Visualizing Time Series Data in Python
• [ ] Datacamp: Improving Your Data Visualizations in Python
• [ ] Datacamp: Visualizing Geospatial Data in Python
• [ ] Datacamp: Interactive Data Visualization with Bokeh
• [ ] Udacity: Data Visualization in Tableau
• [ ] Youtube: Jake VanderPlas - Exploratory Data Visualization with Vega, Vega-Lite, and Altair - PyCon 2018
• [ ] UWData: Data Visualization Curriculum

Be able to to read research papers

• [X] Paper: A Neural Probabilistic Language Model
• [ ] Paper: Efficient Estimation of Word Representations in Vector Space
• [X] Paper: Sequence to Sequence Learning with Neural Networks
• [X] Paper: Neural Machine Translation by Jointly Learning to Align and Translate
• [X] Paper: Attention Is All You Need
• [X] Paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• [X] Paper: XLNet: Generalized Autoregressive Pretraining for Language Understanding
• [X] Paper: Synonyms Based Term Weighting Scheme: An Extension to TF.IDF
• [ ] Paper: RoBERTa: A Robustly Optimized BERT Pretraining Approach
• [ ] Paper: GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
• [X] Paper: Amazon.com Recommendations Item-to-Item Collaborative Filtering
• [X] Paper: Collaborative Filtering for Implicit Feedback Datasets
• [ ] Paper: BPR: Bayesian Personalized Ranking from Implicit Feedback
• [X] Paper: Factorization Machines
• [X] Paper: Wide & Deep Learning for Recommender Systems
• [X] Paper: Neural Factorization Machines for Sparse Predictive Analytics
• [X] Paper: Multiword Expressions: A Pain in the Neck for NLP
• [X] Paper: PyTorch: An Imperative Style, High-Performance Deep Learning Library
• [X] Paper: ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS
• [X] Paper: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
• [X] Paper: A Simple Framework for Contrastive Learning of Visual Representations
• [X] Paper: Self-Supervised Learning of Pretext-Invariant Representations
• [X] Paper: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
• [X] Paper: Self-Labelling via Simultaneous Clustering and Representation Learning
• [ ] Paper: A Survey on Contextual Embeddings
• [X] Paper: A survey on Semi-, Self- and Unsupervised Techniques in Image Classification
• [ ] Paper: Shortcut Learning in Deep Neural Networks
• [X] Paper: Multi-document Summarization by using TextRank and Maximal Marginal Relevance for Text in Bahasa Indonesia
• [X] Paper: Train Once, Test Anywhere: Zero-Shot Learning for Text Classification
• [X] Paper: Zero-shot Text Classification With Generative Language Models
• [X] Paper: How to Fine-Tune BERT for Text Classification?
• [X] Paper: Universal Sentence Encoder
• [X] Paper: Enriching Word Vectors with Subword Information
• [ ] Paper: Deep Learning Based Text Classification: A Comprehensive Review
• [X] Paper: Beyond Accuracy: Behavioral Testing of NLP models with CheckList
• [X] Paper: Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks
• [ ] Paper: Temporal Ensembling for Semi-Supervised Learning
• [X] Whitepaper: Architecting for the Cloud AWS Best Practices
• [X] Whitepaper: AWS Well-Architected Framework
• [X] Whitepaper: AWS Security Best Practices
• [X] Whitepaper: Blue/Green Deployments on AWS
• [X] Whitepaper: Microservices on AWS
• [X] Whitepaper: Optimizing Enterprise Economics with Serverless Architectures
• [X] Whitepaper: Practicing Continuous Integration and Continuous Delivery on AWS
• [X] Whitepaper: Running Containerized Microservices on AWS
• [X] Whitepaper: Serverless Architectures with AWS Lambda

Be able to model problems mathematically

• [ ] 3Blue1Brown: Essence of Calculus
  • [ ] The Essence of Calculus, Chapter 1 0:17:04
  • [ ] The paradox of the derivative | Essence of calculus, chapter 2 0:17:57
  • [ ] Derivative formulas through geometry | Essence of calculus, chapter 3 0:18:43
  • [ ] Visualizing the chain rule and product rule | Essence of calculus, chapter 4 0:16:52
  • [ ] What's so special about Euler's number e? | Essence of calculus, chapter 5 0:13:50
  • [ ] Implicit differentiation, what's going on here? | Essence of calculus, chapter 6 0:15:33
  • [ ] Limits, L'Hôpital's rule, and epsilon delta definitions | Essence of calculus, chapter 7 0:18:26
  • [ ] Integration and the fundamental theorem of calculus | Essence of calculus, chapter 8 0:20:46
  • [ ] What does area have to do with slope? | Essence of calculus, chapter 9 0:12:39
  • [ ] Higher order derivatives | Essence of calculus, chapter 10 0:05:38
  • [ ] Taylor series | Essence of calculus, chapter 11 0:22:19
  • [ ] What they won't teach you in calculus 0:16:22
• [ ] 3Blue1Brown: Essence of linear algebra
  • [ ] Vectors, what even are they? | Essence of linear algebra, chapter 1 0:09:52
  • [ ] Linear combinations, span, and basis vectors | Essence of linear algebra, chapter 2 0:09:59
  • [ ] Linear transformations and matrices | Essence of linear algebra, chapter 3 0:10:58
  • [ ] Matrix multiplication as composition | Essence of linear algebra, chapter 4 0:10:03
  • [ ] Three-dimensional linear transformations | Essence of linear algebra, chapter 5 0:04:46
  • [ ] The determinant | Essence of linear algebra, chapter 6 0:10:03
  • [ ] Inverse matrices, column space and null space | Essence of linear algebra, chapter 7 0:12:08
  • [ ] Nonsquare matrices as transformations between dimensions | Essence of linear algebra, chapter 8 0:04:27
  • [ ] Dot products and duality | Essence of linear algebra, chapter 9 0:14:11
  • [ ] Cross products | Essence of linear algebra, Chapter 10 0:08:53
  • [ ] Cross products in the light of linear transformations | Essence of linear algebra chapter 11 0:13:10
  • [ ] Cramer's rule, explained geometrically | Essence of linear algebra, chapter 12 0:12:12
  • [ ] Change of basis | Essence of linear algebra, chapter 13 0:12:50
  • [ ] Eigenvectors and eigenvalues | Essence of linear algebra, chapter 14 0:17:15
  • [ ] Abstract vector spaces | Essence of linear algebra, chapter 15 0:16:46
• [ ] 3Blue1Brown: Neural networks
  • [ ] But what is a Neural Network? | Deep learning, chapter 1 0:19:13
  • [ ] Gradient descent, how neural networks learn | Deep learning, chapter 2 0:21:01
  • [ ] What is backpropagation really doing? | Deep learning, chapter 3 0:13:54
  • [ ] Backpropagation calculus | Deep learning, chapter 4 0:10:17
• [X] Article: A Visual Tour of Backpropagation
• [ ] Article: Relearning Matrices as Linear Functions
• [ ] Article: You Could Have Come Up With Eigenvectors - Here's How
• [ ] Article: PageRank - How Eigenvectors Power the Algorithm Behind Google Search
• [ ] Article: Interactive Visualization of Why Eigenvectors Matter
• [ ] Article: Cross-Entropy and KL Divergence
• [ ] Article: Why Randomness Is Information?
• [ ] Article: Basic Probability Theory
• [ ] Book: Basics of Linear Algebra for Machine Learning
• [ ] Datacamp: Foundations of Probability in Python
• [ ] Datacamp: Statistical Thinking in Python (Part 1)
• [ ] Datacamp: Statistical Thinking in Python (Part 2)
• [ ] Datacamp: Statistical Simulation in Python
• [X] edX: Essential Statistics for Data Analysis using Excel
• [ ] Computational Linear Algebra for Coders
• [ ] Khan Academy: Precalculus
• [ ] Khan Academy: Probability
• [ ] Khan Academy: Differential Calculus
• [ ] Khan Academy: Multivariable Calculus
• [ ] Khan Academy: Linear Algebra
• [ ] MIT: 18.06 Linear Algebra (Professor Strang)
  • [X] 1. The Geometry of Linear Equations 0:39:49
  • [X] 2. Elimination with Matrices. 0:47:41
  • [X] 3. Multiplication and Inverse Matrices 0:46:48
  • [X] 4. Factorization into A = LU 0:48:05
  • [X] 5. Transposes, Permutations, Spaces R^n 0:47:41
  • [X] 6. Column Space and Nullspace 0:46:01
  • [X] 9. Independence, Basis, and Dimension 0:50:14
  • [X] 10. The Four Fundamental Subspaces 0:49:20
  • [X] 11. Matrix Spaces; Rank 1; Small World Graphs 0:45:55
  • [X] 14. Orthogonal Vectors and Subspaces 0:49:47
  • [X] 15. Projections onto Subspaces 0:48:51
  • [X] 16. Projection Matrices and Least Squares 0:48:05
  • [X] 17. Orthogonal Matrices and Gram-Schmidt 0:49:09
  • [X] 21. Eigenvalues and Eigenvectors 0:51:22
  • [ ] 22. Diagonalization and Powers of A 0:51:50
  • [ ] 24. Markov Matrices; Fourier Series 0:51:11
  • [ ] 25. Symmetric Matrices and Positive Definiteness 0:43:52
  • [ ] 27. Positive Definite Matrices and Minima 0:50:40
  • [ ] 29. Singular Value Decomposition 0:40:28
  • [ ] 30. Linear Transformations and Their Matrices 0:49:27
  • [ ] 31. Change of Basis; Image Compression 0:50:13
  • [ ] 33. Left and Right Inverses; Pseudoinverse 0:41:52
• [ ] StatQuest: Statistics Fundamentals
  • [ ] StatQuest: Histograms, Clearly Explained 0:03:42
  • [ ] StatQuest: What is a statistical distribution? 0:05:14
  • [ ] StatQuest: The Normal Distribution, Clearly Explained!!! 0:05:12
  • [ ] Statistics Fundamentals: Population Parameters 0:14:31
  • [ ] Statistics Fundamentals: The Mean, Variance and Standard Deviation 0:14:22
  • [ ] StatQuest: What is a statistical model? 0:03:45
  • [ ] StatQuest: Sampling A Distribution 0:03:48
  • [ ] Hypothesis Testing and The Null Hypothesis 0:14:40
  • [ ] Alternative Hypotheses: Main Ideas!!! 0:09:49
  • [ ] p-values: What they are and how to interpret them 0:11:22
  • [ ] How to calculate p-values 0:25:15
  • [ ] p-hacking: What it is and how to avoid it! 0:13:44
  • [ ] Statistical Power, Clearly Explained!!! 0:08:19
  • [ ] Power Analysis, Clearly Explained!!! 0:16:44
  • [ ] Covariance and Correlation Part 1: Covariance 0:22:23
  • [ ] Covariance and Correlation Part 2: Pearson's Correlation 0:19:13
  • [ ] StatQuest: R-squared explained 0:11:01
  • [ ] The Central Limit Theorem 0:07:35
  • [ ] StatQuickie: Standard Deviation vs Standard Error 0:02:52
  • [ ] StatQuest: The standard error 0:11:43
  • [ ] Bam!!! Clearly Explained!!! 0:02:49
  • [ ] StatQuest: Technical and Biological Replicates 0:05:27
  • [ ] StatQuest - Sample Size and Effective Sample Size, Clearly Explained 0:06:32
  • [ ] Bar Charts Are Better than Pie Charts 0:01:45
  • [ ] StatQuest: Boxplots, Clearly Explained 0:02:33
  • [ ] StatQuest: Logs (logarithms), clearly explained 0:15:37
  • [ ] StatQuest: Confidence Intervals 0:06:41
  • [ ] StatQuickie: Thresholds for Significance 0:06:40
  • [ ] StatQuickie: Which t test to use 0:05:10
  • [ ] StatQuest: One or Two Tailed P-Values 0:07:05
  • [ ] The Binomial Distribution and Test, Clearly Explained!!! 0:15:46
  • [ ] StatQuest: Quantiles and Percentiles, Clearly Explained!!! 0:06:30
  • [ ] StatQuest: Quantile-Quantile Plots (QQ plots), Clearly Explained 0:06:55
  • [ ] StatQuest: Quantile Normalization 0:04:51
  • [ ] StatQuest: Probability vs Likelihood 0:05:01
  • [ ] StatQuest: Maximum Likelihood, clearly explained!!! 0:06:12
  • [ ] Maximum Likelihood for the Exponential Distribution, Clearly Explained! V2.0 0:09:39
  • [ ] Why Dividing By N Underestimates the Variance 0:17:14
  • [ ] Maximum Likelihood for the Binomial Distribution, Clearly Explained!!! 0:11:24
  • [ ] Maximum Likelihood For the Normal Distribution, step-by-step! 0:19:50
  • [ ] StatQuest: Odds and Log(Odds), Clearly Explained!!! 0:11:30
  • [ ] StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!! 0:16:20
  • [ ] Live 2020-04-20!!! Expected Values 0:33:00
• [ ] Udacity: Algebra Review
• [ ] Udacity: Differential Equations in Action
• [X] Udacity: Eigenvectors and Eigenvalues
• [ ] Udacity: Linear Algebra Refresher
• [ ] Udacity: Statistics
• [ ] Udacity: Intro to Descriptive Statistics
• [ ] Udacity: Intro to Inferential Statistics
• [ ] Youtube: Principal Component Analysis (PCA) - THE MATH YOU SHOULD KNOW! 0:10:06
• [ ] Youtube: Support Vector Machines - THE MATH YOU SHOULD KNOW 0:11:21
• [ ] Youtube: The Kernel Trick - THE MATH YOU SHOULD KNOW! 0:07:29
• [ ] Youtube: Logistic Regression - THE MATH YOU SHOULD KNOW! 0:09:14
• [ ] Youtube: But what is a Neural Network? - THE MATH YOU SHOULD KNOW! 0:19:07

Be able to structure machine learning projects

• [ ] Article: Organizing machine learning projects: project management guidelines
• [ ] Article: Building machine learning products: a problem well-defined is a problem half-solved.
• [ ] Coursera: Structuring Machine Learning Projects
• [X] Datacamp: Conda Essentials
• [ ] Datacamp: Conda for Building & Distributing Packages
• [X] Datacamp: Creating Robust Python Workflows
• [X] Datacamp: Software Engineering for Data Scientists in Python
• [X] Datacamp: Designing Machine Learning Workflows in Python
• [X] Datacamp: Object-Oriented Programming in Python
• [ ] Datacamp: Command Line Automation in Python
• [ ] Datacamp: Introduction to Data Engineering
• [ ] Datacamp: Experimental Design in Python
• [X] Full Stack Deep Learning Bootcamp: March 2019
  • [X] Lecture 1: Introduction to Deep Learning
  • [X] Lecture 2: Setting Up Machine Learning Projects
  • [X] Lecture 3: Introduction to the Text Recognizer Project
  • [X] Lecture 4: Infrastructure and Tooling
  • [X] Lecture 5: Tracking Experiments
  • [X] Lecture 6: Data Management
  • [X] Lecture 7: Machine Learning Teams
  • [X] Lecture 9: Lukas Biewald
  • [X] Lecture 10: Troubleshooting Deep Neural Networks
  • [X] Lecture 11: Labs 6-9: Detection, Data Labeling, Testing and Deployment
  • [X] Lecture 12: Testing and Deployment
  • [X] Lecture 13: Research Directions
  • [X] Lecture 14: Jeremy Howard
  • [X] Lecture 15: Richard Socher
  • [X] Guest Lecture - Chip Huyen - Machine Learning Interviews - Full Stack Deep Learning
• [ ] MIT: The Missing Semester of CS Education
  • [ ] Lecture 1: Course Overview + The Shell (2020) 0:48:16
  • [ ] Lecture 2: Shell Tools and Scripting (2020) 0:48:55
  • [ ] Lecture 3: Editors (vim) (2020) 0:48:26
  • [ ] Lecture 4: Data Wrangling (2020) 0:50:03
  • [ ] Lecture 5: Command-line Environment (2020) 0:56:06
  • [ ] Lecture 6: Version Control (git) (2020) 1:24:59
  • [ ] Lecture 7: Debugging and Profiling (2020) 0:54:13
  • [ ] Lecture 8: Metaprogramming (2020) 0:49:52
  • [ ] Lecture 9: Security and Cryptography (2020) 1:00:59
  • [ ] Lecture 10: Potpourri (2020) 0:57:54
  • [ ] Lecture 11: Q&A (2020) 0:53:52
• [X] Treehouse: Object Oriented Python
• [X] Treehouse: Setup Local Python Environment
• [ ] Udacity: Writing READMEs
• [X] Youtube: Weights and Biases Tutorial
• [ ] Youtube: MLOps Tutorials
  • [ ] MLOps Tutorial #1: Intro to Continuous Integration for ML 17:44:00
  • [ ] MLOps Tutorial #2: When data is too big for Git 10:51:00
  • [ ] MLOps Tutorial #3: Track ML models with Git & GitHub Actions 14:12:00

Be able to utilize version control

• [ ] Article: Mastering Git Stash Workflow
• [X] Codecademy: Learn Git
• [X] Code School: Git Real
• [X] Datacamp: Introduction to Git for Data Science
• [ ] Learn enough git to be dangerous
• [ ] Thoughtbot: Mastering Git
• [X] Udacity: GitHub & Collaboration
• [X] Udacity: How to Use Git and GitHub
• [X] Udacity: Version Control with Git

Be familiar with a breadth of models and algorithms

• [ ] Article: Label Smoothing Explained using Microsoft Excel
• [ ] Article: Naive Bayes classification
• [ ] Article: Linear regression
• [ ] Article: Polynomial regression
• [ ] Article: Logistic regression
• [ ] Article: Decision trees
• [ ] Article: K-nearest neighbors
• [ ] Article: Support Vector Machines
• [ ] Article: Random forests
• [ ] Article: Boosted trees
• [ ] Article: Neural networks: activation functions
• [ ] Article: Neural networks: training with backpropagation
• [ ] Article: Gradient descent
• [ ] Article: Setting the learning rate of your neural network
• [ ] Article: Deep neural networks: preventing overfitting
• [ ] Article: Normalizing your data (specifically, input and batch normalization)
• [ ] Article: Batch Normalization
• [ ] Article: Baidu Deep Voice explained: Part 1 — the Inference Pipeline
• [ ] Article: Baidu Deep Voice explained Part 2 — Training
• [ ] Article: Hacker's Guide to Fundamental Machine Learning Algorithms with Python
• [ ] Article: Are Deep Neural Networks Dramatically Overfitted?
• [ ] Article: Attention? Attention!
• [ ] Article: How to Explain the Prediction of a Machine Learning Model?
• [ ] Article: Neural Network from scratch-part 1
• [ ] Article: Neural Network from scratch-part 2
• [ ] Article: Explain Neural Arithmetic Logic Units (NALU)
• [ ] Article: Predict Bitcoin price with Long sort term memory Networks (LSTM)
• [ ] Article: Graph Neural Networks - An overview
• [ ] Article: Deep Learning Algorithms - The Complete Guide
• [X] AWS: Semantic Segmentation Explained
• [X] AWS: The Elements of Data Science
• [X] AWS: Understanding Neural Networks
• [ ] Book: Pattern Recognition and Machine Learning
• [ ] Coursera: Neural Networks and Deep Learning
• [X] Datacamp: AI Fundamentals
• [X] Datacamp: Kaggle Competition
• [ ] Datacamp: Extreme Gradient Boosting with XGBoost
• [ ] Datacamp: Introduction to PySpark
• [ ] Datacamp: Building Recommendation Engines with PySpark
• [ ] Datacamp: Foundations of Predictive Analytics in Python (Part 1)
• [ ] Datacamp: Foundations of Predictive Analytics in Python (Part 2)
• [X] Datacamp: Ensemble Methods in Python
• [ ] Datacamp: HR Analytics in Python: Predicting Employee Churn
• [ ] Datacamp: Predicting Customer Churn in Python
• [X] Elements of AI
• [X] edX: Principles of Machine Learning
• [X] edX: Data Science Essentials
• [X] edX: Implementing Predictive Analytics with Spark in Azure HDInsight
• [X] DeepMind: Inefficient Data Efficiency
• [ ] DeepMind: DeepMind x UCL | Deep Learning Lecture Series 2020
  • [ ] DeepMind x UCL | Deep Learning Lectures | 1/12 | Intro to Machine Learning & AI 1:25:17
  • [ ] DeepMind x UCL | Deep Learning Lectures | 2/12 | Neural Networks Foundations 1:24:12
  • [ ] DeepMind x UCL | Deep Learning Lectures | 3/12 | Convolutional Neural Networks for Image Recognition 1:20:19
  • [ ] DeepMind x UCL | Deep Learning Lectures | 4/12 | Advanced Models for Computer Vision 1:33:37
  • [ ] DeepMind x UCL | Deep Learning Lectures | 5/12 | Optimization for Machine Learning 1:30:21
  • [ ] DeepMind x UCL | Deep Learning Lectures | 6/12 | Sequences and Recurrent Networks 1:20:27
  • [X] DeepMind x UCL | Deep Learning Lectures | 7/12 | Deep Learning for Natural Language Processing 1:32:29
  • [ ] DeepMind x UCL | Deep Learning Lectures | 8/12 | Attention and Memory in Deep Learning 1:36:04
  • [ ] DeepMind x UCL | Deep Learning Lectures | 9/12 | Generative Adversarial Networks 1:42:26
  • [ ] DeepMind x UCL | Deep Learning Lectures | 10/12 | Unsupervised Representation Learning 1:44:40
  • [ ] DeepMind x UCL | Deep Learning Lectures | 11/12 | Modern Latent Variable Models 1:28:26
  • [ ] DeepMind x UCL | Deep Learning Lectures | 12/12 | Responsible Innovation 1:02:28
• [ ] Fast.ai: Deep Learning for Coder (2020)
  • [X] Lesson 1
  • [X] Lesson 2
  • [ ] Lesson 3
  • [ ] Lesson 4
  • [ ] Lesson 5
  • [ ] Lesson 6
  • [ ] Lesson 7
  • [ ] Lesson 8
• [ ] Google: Launching into Machine Learning
• [ ] Book: Grokking Deep Learning
• [ ] Book: Make Your Own Neural Network
• [ ] MIT: 6.S191: Introduction to Deep Learning
  • [ ] MIT Introduction to Deep Learning | 6.S191 0:52:51
  • [ ] Recurrent Neural Networks | MIT 6.S191 0:45:28
  • [ ] Convolutional Neural Networks | MIT 6.S191 0:37:20
  • [ ] Deep Generative Modeling | MIT 6.S191 0:40:39
  • [ ] Reinforcement Learning | MIT 6.S191 0:44:11
  • [ ] Deep Learning New Frontiers | MIT 6.S191 0:38:10
  • [ ] Neurosymbolic AI | MIT 6.S191 0:41:10
  • [ ] Generalizable Autonomy for Robot Manipulation | MIT 6.S191 0:47:00
  • [ ] Neural Rendering | MIT 6.S191 0:36:44
  • [ ] Machine Learning for Scent | MIT 6.S191 0:38:51
• [X] Pluralsight: Understanding Algorithms for Recommendation Systems
• [X] Pluralsight: Deep Learning: The Big Picture
• [ ] StatQuest: Machine Learning
  • [X] A Gentle Introduction to Machine Learning 0:12:45
  • [X] Machine Learning Fundamentals: Cross Validation 0:06:04
  • [X] Machine Learning Fundamentals: The Confusion Matrix 0:07:12
  • [X] Machine Learning Fundamentals: Sensitivity and Specificity 0:11:46
  • [X] Machine Learning Fundamentals: Bias and Variance 0:06:36
  • [ ] ROC and AUC, Clearly Explained! 0:16:26
  • [ ] StatQuest: Fitting a line to data, aka least squares, aka linear regression. 0:09:21
  • [ ] StatQuest: Linear Models Pt.1 - Linear Regression 0:27:26
  • [ ] StatQuest: Odds and Log(Odds), Clearly Explained!!! 0:11:30
  • [ ] StatQuest: Odds Ratios and Log(Odds Ratios), Clearly Explained!!! 0:16:20
  • [ ] StatQuest: Logistic Regression 0:08:47
  • [ ] Logistic Regression Details Pt1: Coefficients 0:19:02
  • [ ] Logistic Regression Details Pt 2: Maximum Likelihood 0:10:23
  • [ ] Logistic Regression Details Pt 3: R-squared and p-value 0:15:25
  • [ ] Saturated Models and Deviance 0:18:39
  • [ ] Deviance Residuals 0:06:18
  • [ ] Regularization Part 1: Ridge (L2) Regression 0:20:26
  • [ ] Regularization Part 2: Lasso (L1) Regression 0:08:19
  • [ ] Ridge vs Lasso Regression, Visualized!!! 0:09:05
  • [ ] Regularization Part 3: Elastic Net Regression 0:05:19
  • [ ] StatQuest: Principal Component Analysis (PCA), Step-by-Step 0:21:57
  • [ ] StatQuest: PCA main ideas in only 5 minutes!!! 0:06:04
  • [ ] StatQuest: PCA - Practical Tips 0:08:19
  • [ ] StatQuest: PCA in Python 0:11:37
  • [ ] StatQuest: Linear Discriminant Analysis (LDA) clearly explained. 0:15:12
  • [ ] StatQuest: MDS and PCoA 0:08:18
  • [ ] StatQuest: t-SNE, Clearly Explained 0:11:47
  • [ ] StatQuest: Hierarchical Clustering 0:11:19
  • [ ] StatQuest: K-means clustering 0:08:57
  • [ ] StatQuest: K-nearest neighbors, Clearly Explained 0:05:30
  • [ ] Naive Bayes, Clearly Explained!!! 0:15:12
  • [ ] Gaussian Naive Bayes, Clearly Explained!!! 0:09:41
  • [ ] StatQuest: Decision Trees 0:17:22
  • [ ] StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data 0:05:16
  • [ ] Regression Trees, Clearly Explained!!! 0:22:33
  • [ ] How to Prune Regression Trees, Clearly Explained!!! 0:16:15
  • [ ] StatQuest: Random Forests Part 1 - Building, Using and Evaluating 0:09:54
  • [ ] StatQuest: Random Forests Part 2: Missing data and clustering 0:11:53
  • [ ] The Chain Rule 0:18:23
  • [ ] Gradient Descent, Step-by-Step 0:23:54
  • [ ] Stochastic Gradient Descent, Clearly Explained!!! 0:10:53
  • [ ] AdaBoost, Clearly Explained 0:20:54
  • [ ] Gradient Boost Part 1: Regression Main Ideas 0:15:52
  • [ ] Gradient Boost Part 2: Regression Details 0:26:45
  • [ ] Gradient Boost Part 3: Classification 0:17:02
  • [ ] Gradient Boost Part 4: Classification Details 0:36:59
  • [ ] Bam!!! Clearly Explained!!! 0:02:49
  • [ ] Support Vector Machines, Clearly Explained!!! 0:20:32
  • [ ] Support Vector Machines Part 2: The Polynomial Kernel 0:07:15
  • [ ] Support Vector Machines Part 3: The Radial (RBF) Kernel 0:15:52
  • [ ] XGBoost Part 1: Regression 0:25:46
  • [ ] XGBoost Part 2: Classification 0:25:17
  • [ ] XGBoost Part 3: Mathematical Details 0:27:24
  • [ ] XGBoost Part 4: Crazy Cool Optimizations 0:24:27
  • [ ] StatQuest: Fiitting a curve to data, aka lowess, aka loess 0:10:10
  • [ ] Statistics Fundamentals: Population Parameters 0:14:31
  • [ ] Principal Component Analysis (PCA) clearly explained (2015) 0:20:16
  • [ ] Decision Trees in Python from Start to Finish 1:06:23
• [ ] Udacity: A Friendly Introduction to Machine Learning
• [ ] Udacity: Intro to Data Analysis
• [ ] Udacity: Intro to Data Science
• [ ] Udacity: Intro to Machine Learning
• [ ] Udacity: Reinforcement Learning
• [ ] Udacity: Deep Learning
• [ ] Udacity: Intro to Artificial Intelligence
• [ ] Udacity: Classification Models
• [X] Youtube: DETR: End-to-End Object Detection with Transformers (Paper Explained)
• [X] Youtube: Sebastian Ruder: Neural Semi-supervised Learning under Domain Shift
• [X] Youtube: How do we check if a neural network has learned a specific phenomenon?
• [X] Youtube: What is Adversarial Machine Learning and what to do about it? – Adversarial example compilation
• [ ] Youtube: AI fabricates music in a celebrity's voice (JukeboxAI) 0:15:54
• [ ] Youtube: Activation Functions - EXPLAINED! 0:10:05
• [ ] Youtube: Batch Normalization - EXPLAINED! 0:08:48
• [ ] Youtube: Optimizers - EXPLAINED! 0:07:22
• [ ] Youtube: Loss Functions - EXPLAINED! 0:08:30
• [ ] Youtube: Boosting - EXPLAINED! 0:17:31
• [ ] Youtube: Gradient Descent - THE MATH YOU SHOULD KNOW 0:20:08
• [ ] Youtube: Logistic Regression - VISUALIZED! 0:18:31
• [ ] Youtube: Linear Regression and Multiple Regression 0:12:54
• [ ] Youtube: Precision, Recall & F-Measure 0:13:42
• [ ] Youtube: Bootstrapping, Bagging and Random Forests 0:21:45
• [ ] Youtube: Deep Mind's AlphaGo Zero - EXPLAINED 0:11:13
• [ ] Youtube: Curiosity in AI 0:06:16
• [ ] Youtube: DropBlock - A BETTER DROPOUT for Neural Networks 0:07:45
• [ ] Youtube: Neural Voice Cloning 0:19:56
• [ ] Youtube: Neural Networks from Scratch in Python
  • [ ] Neural Networks from Scratch - P.1 Intro and Neuron Code 0:16:59
  • [ ] Neural Networks from Scratch - P.2 Coding a Layer 0:15:06
  • [ ] Neural Networks from Scratch - P.3 The Dot Product 0:25:17
  • [ ] Neural Networks from Scratch - P.4 Batches, Layers, and Objects 0:33:46
  • [ ] Neural Networks from Scratch - P.5 Hidden Layer Activation Functions 0:40:05
• [ ] Youtube: Visualizing Deep Learning
  • [ ] The Neural Network, A Visual Introduction | Visualizing Deep Learning, Chapter 1
• [X] Youtube: Deep Double Descent

Be able to implement models in scikit-learn

• [X] Datacamp: Supervised Learning with scikit-learn
• [X] Datacamp: Machine Learning with Tree-Based Models in Python
• [ ] Datacamp: Introduction to Linear Modeling in Python
• [X] Datacamp: Linear Classifiers in Python
• [ ] Datacamp: Generalized Linear Models in Python
• [X] Pluralsight: Building Machine Learning Models in Python with scikit-learn
• [ ] Youtube: Applied Machine Learning 2020
  • [ ] Channel Intro - Applied Machine Learning 0:01:28
  • [ ] Applied ML 2020 - 01 Introduction 1:16:01
  • [ ] Applied ML 2020 - 02 Visualization and matplotlib 1:07:30
  • [ ] Applied ML 2020 - 03 Supervised learning and model validation 1:12:00
  • [ ] Applied ML 2020 - 04 - Preprocessing 1:07:40
  • [ ] Applied ML 2020 - 05 - Linear Models for Regression 1:06:54
  • [ ] Applied ML 2020 - 06 - Linear Models for Classification 1:07:50
  • [ ] Applied ML 2020 - 07 - Decision Trees and Random Forests 1:07:58
  • [ ] Applied ML 2020 - 08 - Gradient Boosting 1:02:12
  • [ ] Applied ML 2020 - 09 - Model Evaluation and Metrics 1:18:23
  • [ ] Applied ML 2020 - 10 - Calibration, Imbalanced data 1:16:14
  • [ ] Applied ML 2020 - 11 - Model Inspection and Feature Selection 1:15:15
  • [ ] Applied ML 2020 - 12 - AutoML (plus some feature selection) 1:25:38
  • [ ] Applied ML 2020 - 13 - Dimensionality reduction 1:30:34
  • [ ] Applied ML 2020 - 14 - Clustering and Mixture Models 1:26:33
  • [ ] Applied ML 2020 - 15 - Working with Text Data 1:27:08
  • [ ] Applied ML 2020 - 16 - Topic models for text data 1:18:34
  • [ ] Applied ML 2020 - 17 - Word vectors and document embeddings 1:03:04
  • [ ] Applied ML 2020 - 18 - Neural Networks 1:19:36
  • [ ] Applied ML 2020 - 19 - Keras and Convolutional neural nets 1:16:01
  • [ ] Applied ML 2020 - 20 - Advanced neural networks 1:36:28
  • [ ] Applied ML 2020 - 21 - Time Series and Forecasting 1:12:36

Be able to implement models in Tensorflow and Keras

• [X] Coursera: Introduction to Tensorflow
• [X] Coursera: Convolutional Neural Networks in TensorFlow
• [ ] Coursera: Getting Started With Tensorflow 2
• [ ] Coursera: Customising your models with TensorFlow 2
• [X] Deeplizard: Keras - Python Deep Learning Neural Network API
• [ ] Book: Deep Learning with Python (Page: 276)
• [X] Datacamp: Deep Learning in Python
• [X] Datacamp: Convolutional Neural Networks for Image Processing
• [X] Datacamp: Introduction to TensorFlow in Python
• [X] Datacamp: Introduction to Deep Learning with Keras
• [X] Datacamp: Advanced Deep Learning with Keras
• [ ] Google: Intro to Tensorflow
• [ ] Google: Machine Learning Crash Course
• [X] Pluralsight: Deep Learning with Keras
• [X] Udacity: Intro to TensorFlow for Deep Learning

Be able to implement models in PyTorch

• [X] Article: An introduction to PyTorch Lightning with comparisons to PyTorch
• [X] Datacamp: Introduction to Deep Learning with PyTorch
• [X] Deeplizard: Neural Network Programming - Deep Learning with PyTorch
• [ ] Udacity: Intro to Deep Learning with PyTorch
• [ ] Youtube: PyTorch Lightning 101
  • [X] Training a classification model on MNIST with PyTorch
  • [X] From PyTorch to PyTorch Lightning
  • [X] Lightning Data Modules
  • [X] PyTorch Dropout, Batch size and interactive debugging
• [X] Youtube: SimCLR with PyTorch Lightning
• [ ] Youtube: PyTorch Performance Tuning Guide 26:41:00
• [ ] Youtube: Skin Cancer Detection with PyTorch
  • [ ] [PART 1] Skin Cancer Detection with PyTorch 0:10:21
  • [ ] [PART 2] Skin Cancer Detection with PyTorch 0:21:57
  • [ ] [PART 3] Skin Cancer Detection with PyTorch 0:22:24

Be able to apply unsupervised learning algorithms

• [ ] Article: Grouping data points with k-means clustering
• [ ] Article: Soft clustering with Gaussian mixed models (EM)
• [ ] Article: Introduction to autoencoders
• [ ] Article: Variational autoencoders
• [ ] Article: Principal components analysis (PCA)
• [ ] Article: Deep Inside Autoencoders
• [ ] Article: Build a simple Image Retrieval System with an Autoencoder
• [ ] Article: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
• [ ] Article: A Framework For Contrastive Self-Supervised Learning And Designing A New Approach
• [ ] Article: Understanding self-supervised and contrastive learning with "Bootstrap Your Own Latent" (BYOL)
• [ ] Article: Affinity Propagation Algorithm Explained
• [ ] Article: Algorithm Breakdown: Affinity Propagation
• [ ] Article: From Autoencoder to Beta-VAE
• [ ] Article: Self-Supervised Representation Learning
• [ ] Article: GANs in computer vision - Introduction to generative learning
• [ ] Article: GANs in computer vision - self-supervised adversarial training and high-resolution image synthesis with style incorporation
• [ ] Article: GANs in computer vision - semantic image synthesis and learning a generative model from a single image
• [ ] Article: GANs in computer vision - Improved training with Wasserstein distance, game theory control and progressively growing schemes
• [ ] Article: GANs in computer vision - Conditional image synthesis and 3D object generation
• [ ] Article: Decrypt Generative Adversarial Networks (GAN)
• [ ] Article: How to Generate Images using Autoencoders
• [ ] Article: Deepfakes: Face synthesis with GANs and Autoencoders
• [ ] Berkeley: Deep Unsupervised Learning Spring 2020
  • [X] L1 Introduction -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley, Spring 2020 1:10:02
  • [X] L2 Autoregressive Models -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley, Spring 2020 2:27:23
  • [ ] L3 Flow Models -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley -- Spring 2020 1:56:53
  • [ ] L4 Latent Variable Models (VAE) -- CS294-158-SP20 Deep Unsupervised Learning -- UC Berkeley 2:19:33
  • [ ] Lecture 5 Implicit Models -- GANs Part I --- UC Berkeley, Spring 2020 2:32:32
  • [ ] Lecture 6 Implicit Models / GANs part II --- CS294-158-SP20 Deep Unsupervised Learning -- Berkeley 2:09:14
  • [X] Lecture 7 Self-Supervised Learning -- UC Berkeley Spring 2020 - CS294-158 Deep Unsupervised Learning 2:20:41
  • [ ] L8 Round-up of Strengths and Weaknesses of Unsupervised Learning Methods -- UC Berkeley SP20 0:41:51
  • [X] L9 Semi-Supervised Learning and Unsupervised Distribution Alignment -- CS294-158-SP20 UC Berkeley 2:16:00
  • [ ] L10 Compression -- UC Berkeley, Spring 2020, CS294-158 Deep Unsupervised Learning 3:09:49
  • [X] L11 Language Models -- guest instructor: Alec Radford (OpenAI) --- Deep Unsupervised Learning SP20 2:38:19
  • [ ] L12 Representation Learning for Reinforcement Learning --- CS294-158 UC Berkeley Spring 2020 2:01:56
• [ ] Datacamp: Customer Segmentation in Python
• [X] Datacamp: Unsupervised Learning in Python
• [ ] Google: Clustering
• [ ] Google: Recommendation Systems
• [X] Udacity: Segmentation and Clustering
• [X] Youtube: BYOL: Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (Paper Explained)
• [X] Youtube: A critical analysis of self-supervision, or what we can learn from a single image (Paper Explained)
• [X] Youtube: Week 10 – Lecture: Self-supervised learning (SSL) in computer vision (CV)
• [X] Youtube: CVPR 2020 Tutorial: Towards Annotation-Efficient Learning
• [X] Youtube: Yuki Asano | Self-Supervision | Self-Labelling | Labelling Unlabelled videos | CV | CTDS.Show #81
• [X] Youtube: Contrastive Clustering with SwAV
• [ ] Youtube: Variational Autoencoders - EXPLAINED! 0:17:36
• [X] Youtube: OptaProAnalyticsForum– Learning to watch football: Self-supervised representations for tracking data
• [X] Youtube: Can a Neural Net tell if an image is mirrored? – Visual Chirality
• [X] Youtube: Deep InfoMax: Learning deep representations by mutual information estimation and maximization
• [ ] Deep Learning Lecture Summer 2020
  • [ ] Deep Learning: Unsupervised Learning - Part 1
  • [ ] Deep Learning: Unsupervised Learning - Part 2
  • [ ] Deep Learning: Unsupervised Learning - Part 3
  • [ ] Deep Learning: Unsupervised Learning - Part 4
  • [ ] Deep Learning: Unsupervised Learning - Part 5
  • [ ] Deep Learning: Weakly and Self-Supervised Learning - Part 1
  • [ ] Deep Learning: Weakly and Self-Supervised Learning - Part 2
  • [ ] Deep Learning: Weakly and Self-Supervised Learning - Part 3
  • [ ] Deep Learning: Weakly and Self-Supervised Learning - Part 4
• [ ] ECCV 2020: New Frontiers for Learning with Limited Labels or Data
  • [X] Introduction to New Frontiers on Learning with Limited Labels or Data
  • [X] Self-Supervised Part and Viewpoint Discovery from Image Collections
  • [X] Learning Visual Correspondences across Instances and Video Frames
  • [X] Limitless Labels in a Labelless World: Weak Supervision with Noisy Labels
  • [ ] Inverting Neural Networks for Data-free Knowledge Transfer
  • [ ] Learning Efficiently with Biologically Inspired Feedback
• [ ] Youtube: Self-Supervised Learning - What is Next? - Workshop at ECCV 2020, August 28th
  • [X] Next Challenges for Self-Supervised Learning - Aäron van den Oord 0:20:13
  • [X] Perspectives on Unsupervised Representation Learning - Paolo Favaro 0:42:41
  • [X] Learning and Transferring Visual Representations with Few Labels - Carl Doersch 0:32:53
  • [ ] Multi-view Invariance and Grouping for Self-Supervised Learning - Ishan Misra 0:36:31
  • [ ] Representation Learning beyond Instance Discrimination and Semantic Categorization - Stella Yu 0:43:09
  • [X] Self-Supervision as a Path to a Post-Dataset Era - Alexei Alyosha Efros 0:38:06
  • [ ] Self-Supervision & Modularity: Cornerstones for Generalization in Embodied Agents - Deepak Pathak 0:41:56

Be able to implement computer vision models

• [ ] Article: What is Focal Loss and when should you use it?
• [ ] Article: Squeeze and Excitation Networks Explained with PyTorch Implementation
• [ ] Article: DenseNet Architecture Explained with PyTorch Implementation from TorchVision
• [ ] Article: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• [ ] Article: Group Normalization
• [ ] Article: A Short Introduction to Generative Adversarial Networks
• [ ] Article: Semi-supervised Learning with GANs
• [ ] Article: Densely Connected Convolutional Networks in Tensorflow
• [ ] Article: Convolutional neural networks
• [ ] Article: Common architectures in convolutional neural networks
• [ ] Article: An overview of semantic image segmentation
• [ ] Article: Evaluating image segmentation models
• [ ] Article: An overview of object detection: one-stage methods
• [ ] Article: A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN
• [ ] Article: Object Detection for Dummies Part 1: Gradient Vector, HOG, and SS
• [ ] Article: Object Detection for Dummies Part 2: CNN, DPM and Overfeat
• [ ] Article: Object Detection for Dummies Part 3: R-CNN Family
• [ ] Article: Understanding coordinate systems and DICOM for deep learning medical image analysis
• [ ] Article: Understanding the receptive field of deep convolutional networks
• [ ] Article: Deep learning in medical imaging - 3D medical image segmentation with PyTorch
• [ ] Article: Intuitive Explanation of Skip Connections in Deep Learning
• [ ] Article: Human Pose Estimation
• [ ] Article: YOLO - You only look once (Single shot detectors)
• [ ] Article: Localization and Object Detection with Deep Learning
• [ ] Article: Semantic Segmentation in the era of Neural Networks
• [ ] Article: ECCV 2020: Some Highlights
• [ ] Book: Deep Learning for Computer Vision with Python
• [ ] Book: Practical Python and OpenCV
• [ ] Coursera: Convolutional Neural Networks
• [ ] Datacamp: Biomedical Image Analysis in Python
• [ ] Datacamp: Image Processing in Python
• [X] Google: ML Practicum: Image Classification
• [ ] Stanford: CS231N Winter 2016
  • [X] CS231n Winter 2016: Lecture 1: Introduction and Historical Context 1:19:08
  • [X] CS231n Winter 2016: Lecture 2: Data-driven approach, kNN, Linear Classification 1 0:57:28
  • [ ] CS231n Winter 2016: Lecture 3: Linear Classification 2, Optimization 1:11:23
  • [ ] CS231n Winter 2016: Lecture 4: Backpropagation, Neural Networks 1 1:19:38
  • [ ] CS231n Winter 2016: Lecture 5: Neural Networks Part 2 1:18:37
  • [ ] CS231n Winter 2016: Lecture 6: Neural Networks Part 3 / Intro to ConvNets 1:09:35
  • [ ] CS231n Winter 2016: Lecture 7: Convolutional Neural Networks 1:19:01
  • [ ] CS231n Winter 2016: Lecture 8: Localization and Detection 1:04:57
  • [ ] CS231n Winter 2016: Lecture 9: Visualization, Deep Dream, Neural Style, Adversarial Examples 1:18:20
  • [ ] CS231n Winter 2016: Lecture 10: Recurrent Neural Networks, Image Captioning, LSTM 1:09:54
  • [ ] CS231n Winter 2016: Lecture 11: ConvNets in practice 1:15:03
  • [ ] CS231n Winter 2016: Lecture 12: Deep Learning libraries 1:21:06
  • [ ] CS231n Winter 2016: Lecture 14: Videos and Unsupervised Learning 1:17:36
  • [ ] CS231n Winter 2016: Lecture 13: Segmentation, soft attention, spatial transformers 1:10:59
  • [ ] CS231n Winter 2016: Lecture 15: Invited Talk by Jeff Dean 1:14:49
• [ ] Udacity: Introduction to Computer Vision
• [X] Youtube: Deep Residual Learning for Image Recognition (Paper Explained)
• [X] Youtube: Implementing ResNet from scratch
• [ ] Youtube: ConvNets Scaled Efficiently 0:13:19
• [ ] Youtube: Building an Image Captioner with Neural Networks 0:12:54
• [ ] Youtube: Evolution of Face Generation | Evolution of GANs 0:12:23
• [ ] Youtube: Autoencoders - EXPLAINED 0:10:53
• [ ] Youtube: Unpaired Image-Image Translation using CycleGANs 0:16:22
• [ ] Youtube: AI creates Image Classifiers…by DRAWING? 0:09:04
• [ ] Youtube: The Evolution of Convolution Neural Networks 0:24:02
• [ ] Youtube: Depthwise Separable Convolution - A FASTER CONVOLUTION! 0:12:43
• [ ] Youtube: Mask Region based Convolution Neural Networks - EXPLAINED! 0:09:34
• [ ] Youtube: Sound play with Convolution Neural Networks 0:11:57
• [ ] Youtube: Convolution Neural Networks - EXPLAINED 0:19:20
• [ ] Youtube: Generative Adversarial Networks - FUTURISTIC & FUN AI ! 0:14:20

Be able to implement NLP models

• [ ] Article: The Annotated GPT-2
• [ ] Article: Introduction to recurrent neural networks
• [ ] Article: Aspect-Based Opinion Mining (NLP with Python)
• [X] Article: The Transformer Explained
• [X] Article: Controlling Text Generation with Plug and Play Language Models
• [X] Article: What makes a good conversation?
• [X] Article: NLP for Supervised Learning - A Brief Survey
• [ ] Article: Generating Questions Using Transformers
• [ ] Article: Neural Language Models as Domain-Specific Knowledge Bases
• [ ] Article: Understanding BERT’s Semantic Interpretations
• [ ] Article: Using NLP (BERT) to improve OCR accuracy
• [ ] Article: Hyperparameter Optimization for ?Transformers: A guide
• [ ] Article: Faster and smaller quantized NLP with Hugging Face and ONNX Runtime
• [ ] Article: Learning Word Embedding
• [ ] Article: The Transformer Family
• [ ] Article: Generalized Language Models
• [ ] Article: Document clustering
• [ ] Article: The Unreasonable Effectiveness of Recurrent Neural Networks
• [ ] Article: LSTM Primer With Real Life Application( DeepMind Kidney Injury Prediction )*
• [ ] Article: Making sense of LSTMs by example
• [ ] Article: 3 subword algorithms help to improve your NLP model performance
• [ ] Article: Exploring LSTMs
• [ ] Article: Understanding LSTM Networks
• [ ] Article: 74 Summaries of Machine Learning and NLP Research
• [X] A friendly introduction to Recurrent Neural Networks
• [ ] Coursera: Sequence Models
• [ ] Coursera: Natural Language Processing in TensorFlow
• [ ] CMU: Low-resource NLP Bootcamp 2020
  • [ ] CMU Low resource NLP Bootcamp 2020 (1): NLP Tasks 1:46:06
  • [ ] CMU Low resource NLP Bootcamp 2020 (2): Linguistics - Phonology and Morphology 1:24:08
  • [ ] CMU Low resource NLP Bootcamp 2020 (3): Machine Translation 1:55:59
  • [ ] CMU Low resource NLP Bootcamp 2020 (4): Linguistics - Syntax and Morphosyntax 2:00:21
  • [ ] CMU Low resource NLP Bootcamp 2020 (5): Neural Representation Learning 1:19:57
  • [ ] CMU Low resource NLP Bootcamp 2020 (6): Multilingual NLP 2:04:34
  • [ ] CMU Low resource NLP Bootcamp 2020 (7): Speech Synthesis 2:22:14
  • [ ] CMU Low resource NLP Bootcamp 2020 (8): Speech Recognition 2:16:18
• [ ] CMU: Neural Nets for NLP 2020
  • [X] CMU Neural Nets for NLP 2020 (1): Introduction 1:11:38
  • [ ] CMU Neural Nets for NLP 2020 (2): Language Modeling, Efficiency/Training Tricks 1:18:31
  • [ ] CMU Neural Nets for NLP 2020 (3): Convolutional Neural Networks for Text 0:54:45
  • [ ] CMU Neural Nets for NLP 2020 (4): Recurrent Neural Networks 1:11:28
  • [ ] CMU Neural Nets for NLP 2020 (5): Efficiency Tricks for Neural Nets 1:05:37
  • [ ] CMU Neural Nets for NLP 2020 (7): Attention 1:05:26
  • [ ] CMU Neural Nets for NLP 2020 (8): Distributional Semantics and Word Vectors 1:10:45
  • [ ] CMU Neural Nets for NLP 2020 (9): Sentence and Contextual Word Representations 1:16:19
  • [ ] CMU Neural Nets for NLP 2020 (10): Debugging Neural Nets (for NLP) 1:15:26
  • [ ] CMU Neural Nets for NLP 2020 (11): Structured Prediction with Local Independence Assumptions 1:08:38
  • [ ] CMU Neural Nets for NLP 2020 (12): Generating Trees Incrementally 1:14:13
  • [ ] CMU Neural Nets for NLP 2020 (13): Generating Trees Incrementally 0:51:58
  • [ ] CMU Neural Nets for NLP 2020 (14): Search-based Structured Prediction 1:06:19
  • [ ] CMU Neural Nets for NLP 2020 (15): Minimum Risk Training and Reinforcement Learning 1:09:16
  • [ ] CMU Neural Nets for NLP 2020 (16): Advanced Search Algorithms 1:03:02
  • [ ] CMU Neural Nets for NLP 2020 (17): Adversarial Methods 1:14:55
  • [ ] CMU Neural Nets for NLP 2020 (18): Models w/ Latent Random Variables 1:13:16
  • [ ] CMU Neural Nets for NLP 2020 (19): Unsupervised and Semi-supervised Learning of Structure 1:12:47
  • [ ] CMU Neural Nets for NLP 2020 (20): Multitask and Multilingual Learning 1:02:46
  • [ ] CMU Neural Nets for NLP 2020 (21): Document Level Models 0:52:04
  • [ ] CMU Neural Nets for NLP 2020 (22): Neural Nets + Knowledge Bases 1:18:39
  • [ ] CMU Neural Nets for NLP 2020 (23): Machine Reading w/ Neural Nets 1:06:11
  • [ ] CMU Neural Nets for NLP 2020 (24): Natural Language Generation 1:21:48
  • [ ] CMU Neural Nets for NLP 2020 (25): Model Interpretation 1:04:11
• [ ] CMU Multilingual NLP 2020
  • [ ] CMU Multilingual NLP (1): Introduction 1:17:28
  • [ ] CMU Multilingual NLP (2): Typology - The Space of Language 0:37:12
• [X] Datacamp: Advanced NLP with spaCy
• [X] Datacamp: Building Chatbots in Python
• [X] Datacamp: Clustering Methods with SciPy
• [X] Datacamp: Feature Engineering for NLP in Python
• [X] Datacamp: Machine Translation in Python
• [X] Datacamp: Natural Language Processing Fundamentals in Python
• [X] Datacamp: Natural Language Generation in Python
• [X] Datacamp: RNN for Language Modeling
• [X] Datacamp: Regular Expressions in Python
• [X] Datacamp: Sentiment Analysis in Python
• [ ] Datacamp: Spoken Language Processing in Python
• [ ] RNN and LSTM
• [X] Spacy Tutorial
• [ ] Stanford CS224U: Natural Language Understanding | Spring 2019
  • [X] Lecture 1 – Course Overview | Stanford CS224U: Natural Language Understanding | Spring 2019 1:12:59
  • [ ] Lecture 2 – Word Vectors 1 | Stanford CS224U: Natural Language Understanding | Spring 2019 1:17:10
  • [ ] Lecture 3 – Word Vectors 2 | Stanford CS224U: Natural Language Understanding | Spring 2019 1:16:52
  • [ ] Lecture 4 – Word Vectors 3 | Stanford CS224U: Natural Language Understanding | Spring 2019 0:38:20
  • [ ] Lecture 5 – Sentiment Analysis 1 | Stanford CS224U: Natural Language Understanding | Spring 2019 1:10:44
  • [ ] Lecture 6 – Sentiment Analysis 2 | Stanford CS224U: Natural Language Understanding | Spring 2019 1:03:23
  • [ ] Lecture 7 – Relation Extraction | Stanford CS224U: Natural Language Understanding | Spring 2019 1:19:04
  • [ ] Lecture 8 – NLI 1 | Stanford CS224U: Natural Language Understanding | Spring 2019 1:15:02
  • [ ] Lecture 9 – NLI 2 | Stanford CS224U: Natural Language Understanding | Spring 2019 1:15:35
  • [ ] Lecture 10 – Grounding | Stanford CS224U: Natural Language Understanding | Spring 2019 1:23:15
  • [ ] Lecture 11 – Semantic Parsing | Stanford CS224U: Natural Language Understanding | Spring 2019 1:07:05
  • [ ] Lecture 12 – Evaluation Methods | Stanford CS224U: Natural Language Understanding | Spring 2019 1:18:32
  • [ ] Lecture 13 – Evaluation Metrics | Stanford CS224U: Natural Language Understanding | Spring 2019 1:11:32
  • [ ] Lecture 14 – Contextual Vectors | Stanford CS224U: Natural Language Understanding | Spring 2019 1:14:33
  • [ ] Lecture 15 – Presenting Your Work | Stanford CS224U: Natural Language Understanding | Spring 2019 1:15:11
• [ ] Stanford CS224N: Stanford CS224N: NLP with Deep Learning | Winter 2019
  • [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 1 – Introduction and Word Vectors 1:21:52
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 2 – Word Vectors and Word Senses 1:20:43
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 3 – Neural Networks 1:18:50
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 4 – Backpropagation 1:22:15
  • [X] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 5 – Dependency Parsing 1:20:22
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 6 – Language Models and RNNs 1:08:25
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 7 – Vanishing Gradients, Fancy RNNs 1:13:23
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 8 – Translation, Seq2Seq, Attention 1:16:56
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 9 – Practical Tips for Projects 1:22:39
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 10 – Question Answering 1:21:01
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 11 – Convolutional Networks for NLP 1:20:18
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 12 – Subword Models 1:15:30
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 13 – Contextual Word Embeddings 1:20:18
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 14 – Transformers and Self-Attention 0:53:48
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 15 – Natural Language Generation 1:19:37
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 16 – Coreference Resolution 1:19:20
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 17 – Multitask Learning 1:11:54
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 18 – Constituency Parsing, TreeRNNs 1:20:37
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 19 – Bias in AI 0:56:03
  • [ ] Stanford CS224N: NLP with Deep Learning | Winter 2019 | Lecture 20 – Future of NLP + Deep Learning 1:19:15
• [ ] TextBlob Tutorial Series
  • [ ] Natural Language Processing Tutorial With TextBlob -Tokens,Translation and Ngrams 0:11:01
  • [ ] NLP Tutorial With TextBlob and Python - Parts of Speech Tagging 0:05:59
  • [ ] NLP Tutorial With TextBlob & Python - Lemmatizating 0:06:32
  • [ ] NLP Tutorial with TextBlob & Python - Sentiment Analysis(Polarity,Subjectivity) 0:06:31
  • [ ] Building a NLP-based Flask App with TextBlob 0:37:30
  • [ ] Natural Language Processing with Polyglot - Installation & Intro 0:12:49
• [ ] Treehouse: Regular expression
• [ ] Youtube: fast.ai Code-First Intro to Natural Language Processing
  • [X] What is NLP? (NLP video 1) 0:22:42
  • [X] Topic Modeling with SVD & NMF (NLP video 2) 1:06:39
  • [X] Topic Modeling & SVD revisited (NLP video 3) 0:33:05
  • [X] Sentiment Classification with Naive Bayes (NLP video 4) 0:58:20
  • [ ] Sentiment Classification with Naive Bayes & Logistic Regression, contd. (NLP video 5) 0:51:29
  • [ ] Derivation of Naive Bayes & Numerical Stability (NLP video 6) 0:23:56
  • [ ] Revisiting Naive Bayes, and Regex (NLP video 7) 0:37:33
  • [ ] Intro to Language Modeling (NLP video 8) 0:40:58
  • [ ] Transfer learning (NLP video 9) 1:35:16
  • [ ] ULMFit for non-English Languages (NLP Video 10) 1:49:22
  • [ ] Understanding RNNs (NLP video 11) 0:33:16
  • [ ] Seq2Seq Translation (NLP video 12) 0:59:42
  • [ ] Word embeddings quantify 100 years of gender & ethnic stereotypes-- Nikhil Garg (NLP video 13) 0:47:17
  • [ ] Text generation algorithms (NLP video 14) 0:25:39
  • [ ] Implementing a GRU (NLP video 15) 0:23:13
  • [ ] Algorithmic Bias (NLP video 16) 1:26:17
  • [ ] Introduction to the Transformer (NLP video 17) 0:22:54
  • [ ] The Transformer for language translation (NLP video 18) 0:55:17
  • [ ] What you need to know about Disinformation (NLP video 19) 0:51:21
  • [ ] Article: Zero to Hero with fastai - Beginner
  • [ ] Article: Zero to Hero with fastai - Intermediate
• [ ] NLP Course | For You
  • [ ] Word Embeddings
  • [ ] Text Classification
  • [ ] Language Modeling
  • [ ] Seq2seq and Attention
• [X] Youtube: BERT Research Series
• [X] YouTube: Intro to NLP with Spacy
• [X] Talk: Practical NLP for the Real World
• [X] YouTube: Level 3 AI Assistant Conference 2020
  • [X] Youtube: Conversation Analysis Theory in Chatbots | Michael Szul
  • [X] Youtube: Designing Practical NLP Solutions | Ines Montani
  • [X] Youtube: Effective Copywriting for Chatbots | Hans Van Dam
  • [X] Youtube: Distilling BERT | Sam Sucik
  • [X] Youtube: Transformer Policies that improve Dialogues: A Live Demo by Vincent Warmerdam
  • [X] Youtube: From Research to Production – Our Process at Rasa | Tanja Bunk
  • [X] Youtube: Keynote: Perspective on the 5 Levels of Conversational AI | Alan Nichol
• [ ] Youtube: RASA Algorithm Whiteboard
  • [ ] Introducing The Algorithm Whiteboard 0:01:16
  • [ ] Rasa Algorithm Whiteboard - Diet Architecture 1: How it Works 0:23:27
  • [ ] Rasa Algorithm Whiteboard - Diet Architecture 2: Design Decisions 0:15:06
  • [ ] Rasa Algorithm Whiteboard - Diet Architecture 3: Benchmarking 0:22:34
  • [ ] Rasa Algorithm Whiteboard - Embeddings 1: Just Letters 0:13:48
  • [ ] Rasa Algorithm Whiteboard - Embeddings 2: CBOW and Skip Gram 0:19:24
  • [ ] Rasa Algorithm Whiteboard - Embeddings 3: GloVe 0:19:12
  • [ ] Rasa Algorithm Whiteboard - Embeddings 4: Whatlies 0:14:03
  • [ ] Rasa Algorithm Whiteboard - Attention 1: Self Attention 0:14:32
  • [ ] Rasa Algorithm Whiteboard - Attention 2: Keys, Values, Queries 0:12:26
  • [ ] Rasa Algorithm Whiteboard - Attention 3: Multi Head Attention 0:10:55
  • [ ] Rasa Algorithm Whiteboard: Attention 4 - Transformers 0:14:34
  • [ ] Rasa Algorithm Whiteboard - StarSpace 0:11:46
  • [ ] Rasa Algorithm Whiteboard - TED Policy 0:16:10
  • [ ] Rasa Algorithm Whiteboard - TED in Practice 0:14:54
  • [ ] Rasa Algorithm Whiteboard - Response Selection 0:12:07
  • [ ] Rasa Algorithm Whiteboard - Response Selection: Implementation 0:09:25
  • [ ] Rasa Algorithm Whiteboard - Countvectors 0:13:32
  • [ ] Rasa Algorithm Whiteboard - Subword Embeddings 0:11:58
  • [ ] Rasa Algorithm Whiteboard - Implementation of Subword Embeddings 0:10:01
  • [ ] Rasa Algorithm Whiteboard - BytePair Embeddings 0:12:44
• [X] Youtube: A brief history of the Transformer architecture in NLP
• [X] Youtube: The Transformer neural network architecture explained. “Attention is all you need” (NLP)
• [X] Youtube: How does a Transformer architecture combine Vision and Language? ViLBERT - NLP meets Computer Vision
• [X] Youtube: Strategies for pre-training the BERT-based Transformer architecture – language (and vision)
• [X] Youtube: Ilya Sutskever - GPT-2
• [X] Youtube: NLP Masterclass | Modeling Fallacies in NLP
• [X] Youtube: What is GPT-3? Showcase, possibilities, and implications
• [X] Youtube: TextAttack: A Framework for Data Augmentation and Adversarial Training in NLP
• [X] Youtube: Learning to Rank: From Theory to Production - Malvina Josephidou & Diego Ceccarelli, Bloomberg
• [X] Youtube: Learning "Learning to Rank"
• [X] Youtube: Learning to rank search results - Byron Voorbach & Jettro Coenradie [DevCon 2018]
• [X] Article: How the Embedding Layers in BERT Were Implemented
• [X] Youtube: Easy Data Augmentation for Text Classification
• [X] Youtube: Webinar: Special NLP Session with Hugging Face
• [ ] Youtube: BERT Neural Network - EXPLAINED! 0:11:36
• [ ] Youtube: NLP with Neural Networks & Transformers 0:10:45
• [ ] Youtube: Transformer Neural Networks - EXPLAINED! (Attention is all you need) 0:13:05
• [ ] Youtube: LSTM Networks - EXPLAINED! 0:16:12
• [ ] Youtube: Recurrent Neural Networks - EXPLAINED! 0:17:05
• [ ] Youtube: Attention in Neural Networks 0:11:18
• [X] Youtube: Spacy IRL 2019
  • [X] Sebastian Ruder: Transfer Learning in Open-Source Natural Language Processing (spaCy IRL 2019) 0:31:24
  • [X] Giannis Daras: Improving sparse transformer models for efficient self-attention (spaCy IRL 2019) 0:20:13
  • [X] Peter Baumgartner: Applied NLP: Lessons from the Field (spaCy IRL 2019) 0:18:44
  • [X] Justina Petraitytė: Lessons learned in helping ship conversational AI assistants (spaCy IRL 2019) 0:23:48
  • [X] Yoav Goldberg: The missing elements in NLP (spaCy IRL 2019) 0:30:27
  • [X] Sofie Van Landeghem: Entity linking functionality in spaCy (spaCy IRL 2019) 0:20:08
  • [X] Guadalupe Romero: Rethinking rule-based lemmatization (spaCy IRL 2019) 0:14:49
  • [X] Mark Neumann: ScispaCy: A spaCy pipeline & models for scientific & biomedical text (spaCy IRL 2019) 0:18:59
  • [X] Patrick Harrison: Financial NLP at S&P Global (spaCy IRL 2019) 0:21:42
  • [X] McKenzie Marshall: NLP in Asset Management (spaCy IRL 2019) 0:20:32
  • [X] David Dodson: spaCy in the News: Quartz's NLP pipeline (spaCy IRL 2019) 0:20:56
  • [X] Matthew Honnibal & Ines Montani: spaCy and Explosion: past, present & future (spaCy IRL 2019) 0:54:13
• [ ] Youtube: The Future of Natural Language Processing
• [ ] Youtube: Sentiment Analysis: Key Milestones, Challenges and New Directions
• [ ] Youtube: Simple and Efficient Deep Learning for Natural Language Processing, with Moshe Wasserblat, Intel AI
• [X] Youtube: Why not solve biological problems with a Transformer? BERTology meets Biology
• [ ] Youtube: Introduction to NLP
  • [ ] Introduction to NLP | Bag of Words Model 0:22:23
  • [ ] Introduction to NLP | TF-IDF 0:10:55
  • [ ] Introduction to NLP | Text Cleaning and Preprocessing 0:14:02
  • [ ] Introduction to NLP | Word Embeddings & Word2Vec Model 0:23:09
  • [ ] Introduction to NLP | GloVe Model Explained 0:23:15
  • [ ] Introduction to NLP | GloVe & Word2Vec Transfer Learning 0:21:12
  • [ ] Introduction to NLP | How to Train Custom Word Vectors 0:13:48
  • [ ] Sarcasm is Very Easy to Detect! GloVe + LSTM 0:17:07
  • [ ] Text Summarization & Keyword Extraction | Introduction to NLP 0:14:59
• [ ] Youtube: Self-attention step-by-step | How to get meaning from text
• [ ] Youtube: Chat Bot with PyTorch
• [ ] Youtube: NLP with Friends Talks
  • [ ] NLP with Friends, Featured Friend: Tom McCoy 0:36:48
  • [ ] NLP with Friends, Featured Friend: Maarten Sap 0:36:11
  • [ ] NLP with Friends, featured friend: Nitika Mathur 1:01:42
  • [ ] NLP with Friends, Featured Friend: Sabrina J Mielke 1:01:28
• [X] Youtube: Insincere Question Classification with PyTorch
  • [X] [PART 1] Insincere Question Classification with PyTorch 0:22:34
  • [X] [PART 2] Insincere Question Classification with PyTorch 0:33:53
  • [X] [PART 3] Insincere Question Classification with PyTorch 0:23:49
  • [X] [PART 4] Insincere Question Classification with PyTorch 0:14:25

Be able to model graphs and network data

• [X] Datacamp: Network Analysis in Python (Part 1)
• [ ] Datacamp: Network Analysis in Python (Part 2)

Be able to implement models for timeseries and forecasting

• [ ] Datacamp: Machine Learning for Finance in Python
• [X] Datacamp: Introduction to Time Series Analysis in Python
• [ ] Datacamp: Machine Learning for Time Series Data in Python
• [ ] Datacamp: Intro to Portfolio Risk Management in Python
• [ ] Datacamp: Financial Forecasting in Python
• [X] Datacamp: Predicting CTR with Machine Learning in Python
• [X] Datacamp: Intro to Financial Concepts using Python
• [X] Datacamp: Fraud Detection in Python
• [ ] Datacamp: Forecasting Using ARIMA Models in Python
• [ ] Datacamp: Introduction to Portfolio Analysis in Python
• [ ] Datacamp: Credit Risk Modeling in Python
• [ ] Datacamp: Machine Learning for Marketing in Python
• [ ] Udacity: Machine Learning for Trading
• [ ] Udacity: Time Series Forecasting

Be familiar with Reinforcement Learning

• [X] DeepLizard: Reinforcement Learning - Goal Oriented Intelligence
  • [X] Reinforcement Learning Series Intro - Syllabus Overview 0:05:51
  • [X] Markov Decision Processes (MDPs) - Structuring a Reinforcement Learning Problem 0:06:34
  • [X] Expected Return - What Drives a Reinforcement Learning Agent in an MDP 0:06:47
  • [X] Policies and Value Functions - Good Actions for a Reinforcement Learning Agent 0:06:52
  • [X] What do Reinforcement Learning Algorithms Learn - Optimal Policies 0:06:21
  • [X] Q-Learning Explained - A Reinforcement Learning Technique 0:08:37
  • [X] Exploration vs. Exploitation - Learning the Optimal Reinforcement Learning Policy 0:10:06
  • [X] OpenAI Gym and Python for Q-learning - Reinforcement Learning Code Project 0:07:52
  • [X] Train Q-learning Agent with Python - Reinforcement Learning Code Project 0:08:59
  • [X] Watch Q-learning Agent Play Game with Python - Reinforcement Learning Code Project 0:07:22
  • [X] Deep Q-Learning - Combining Neural Networks and Reinforcement Learning 0:10:50
  • [X] Replay Memory Explained - Experience for Deep Q-Network Training 0:06:21
  • [X] Training a Deep Q-Network - Reinforcement Learning 0:09:07
  • [X] Training a Deep Q-Network with Fixed Q-targets - Reinforcement Learning 0:07:35
  • [X] Deep Q-Network Code Project Intro - Reinforcement Learning 0:06:26
  • [X] Build Deep Q-Network - Reinforcement Learning Code Project 0:16:51
  • [X] Deep Q-Network Image Processing and Environment Management - Reinforcement Learning Code Project 0:21:53
  • [X] Deep Q-Network Training Code - Reinforcement Learning Code Project 0:19:46

Be able to use managed ML services on the cloud

• [X] AWS: Amazon Transcribe Deep Dive: Using Feedback Loops to Improve Confidence Level of Transcription
• [X] AWS: Build a Text Classification Model with AWS Glue and Amazon SageMaker
• [X] AWS: Deep Dive on Amazon Rekognition: Building Computer Visions Based Smart Applications
• [X] AWS: Hands-on Rekognition: Automated Video Editing
• [X] AWS: Introduction to Amazon Comprehend
• [X] AWS: Introduction to Amazon Comprehend Medical
• [X] AWS: Introduction to Amazon Elastic Inference
• [X] AWS: Introduction to Amazon Forecast
• [X] AWS: Introduction to Amazon Lex
• [X] AWS: Introduction to Amazon Personalize
• [X] AWS: Introduction to Amazon Polly
• [X] AWS: Introduction to Amazon SageMaker Ground Truth
• [X] AWS: Introduction to Amazon SageMaker Neo
• [X] AWS: Introduction to Amazon Transcribe
• [X] AWS: Introduction to Amazon Translate
• [X] AWS: Introduction to AWS Marketplace - Machine Learning Category
• [X] AWS: Machine Learning Exam Basics
• [X] AWS: Neural Machine Translation with Sockeye
• [X] AWS: Process Model: CRISP-DM on the AWS Stack
• [X] AWS: Satellite Image Classification in SageMaker
• [X] edX: Amazon SageMaker: Simplifying Machine Learning Application Development

Be able to optimize performance metric

• [ ] Article: Evaluating a machine learning model
• [ ] Article: Hyperparameter tuning for machine learning models
• [ ] Article: Hacker's Guide to Hyperparameter Tuning
• [ ] Coursera: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
• [X] Datacamp: Model Validation in Python
• [X] Datacamp: Hyperparameter Tuning in Python
• [ ] Google: Testing and Debugging
• [ ] Troubleshooting Deep Neural Networks
• [ ] Youtube: How do GPUs speed up Neural Network training? 0:08:19
• [ ] Youtube: Why use GPU with Neural Networks? 0:09:43

Be able to optimize models for production

• [ ] Article: Neural Network Pruning
• [ ] Article: FasterAI
• [X] Article: Is the future of Neural Networks Sparse? An Introduction (1/N)
• [X] Article: Sparse Neural Networks (2/N): Understanding GPU Performance.
• [X] Article: Block Sparse Matrices for Smaller and Faster Language Models

Be able to deploy model to production

• [X] Acloudguru: AWS Certified Machine Learning - Specialty
• [X] Acloudguru: AWS Certified Developer - Associate
• [X] Acloudguru: AWS Certification Preparation Guide
• [X] AWS: Exam Readiness: AWS Certified Developer – Associate
• [X] AWS: Thirty Serverless Architectures in 30 Minutes
• [ ] Article: Deploy a Keras Deep Learning Project to Production with Flask
• [ ] Article: Logging and Debugging in Machine Learning - How to use Python debugger and the logging module to find errors in your AI application
• [ ] Article: How to Unit Test Deep Learning: Tests in TensorFlow, mocking and test coverage
• [ ] Article: Best practices to write Deep Learning code: Project structure, OOP, Type checking and documentation
• [ ] Article: Deep Learning in Production: Laptop set up and system design
• [ ] Article: Enough Docker to be Dangerous
• [ ] Article: How to properly ship and deploy your machine learning model
• [ ] Luigi Patruno: ML in Production
  • [ ] Video: You trained a machine learning model. Now what?
  • [ ] Article: Docker for Machine Learning – Part I
  • [ ] Article: Docker for Machine Learning – Part II
  • [ ] Article: Docker for Machine Learning – Part III
  • [ ] Article: Using Docker to Generate Machine Learning Predictions in Real Time
  • [ ] Article: Batch Inference vs Online Inference
  • [ ] Article: Storing Metadata from Machine Learning Experiments
  • [ ] Article: How Data Leakage Impacts Machine Learning Models
  • [ ] Article: An Introduction to Kubernetes for Data Scientists
  • [ ] Article: How to Use Kubernetes Pods for Machine Learning
  • [ ] Article: Kubernetes Jobs for Machine Learning
  • [ ] Article: Kubernetes CronJobs for Machine Learning
  • [ ] Article: Kubernetes Deployments for Machine Learning
  • [ ] Article: Kubernetes Services for Machine Learning
  • [ ] Article: The Ultimate Guide to Model Retraining
  • [ ] Article: Top ML Resources: Interview with Eric Colson
  • [ ] Article: Top ML Resources: Interview with Veronika Megler, PhD
  • [ ] Article: Top ML Resources: Interview with Erik Bernhardsson
  • [ ] Article: Top ML Resources: Interview with Rui Carmo
  • [ ] Article: Top ML Resources: Interview with Jeremy Jordan
  • [ ] Article: 5 Challenges to Running Machine Learning Systems in Production
  • [ ] Article: Enabling Machine-Learning-as-a-Service Through Privacy Preserving Machine Learning
  • [ ] Article: What Does it Mean to Deploy a Machine Learning Model? (Deployment Series: Guide 01)
  • [ ] Article: Software Interfaces for Machine Learning Deployment (Deployment Series: Guide 02)
  • [ ] Article: Batch Inference for Machine Learning Deployment (Deployment Series: Guide 03)
  • [ ] Article: The Challenges of Online Inference (Deployment Series: Guide 04)
  • [ ] Article: Online Inference for ML Deployment (Deployment Series: Guide 05)
  • [ ] Article: Model Registries for ML Deployment (Deployment Series: Guide 06)
  • [ ] Article: Test-Driven Machine Learning Development (Deployment Series: Guide 07)
  • [ ] Article: A/B Testing Machine Learning Models (Deployment Series: Guide 08)
  • [ ] Article: Lessons Learned from 15 Years of Monitoring Machine Learning in Production
  • [ ] Article: Why is it Important to Monitor Machine Learning Models?
  • [ ] Article: Maximizing Business Impact with Machine Learning
• [ ] Codecademy: Deploy a Website
• [ ] Datacamp: Parallel Computing with Dask
• [X] Datacamp: Cloud Computing for Everyone
• [X] Django Best Practices
• [X] Pluralsight: Docker and Containers: The Big Picture
• [X] Pluralsight: Docker and Kubernetes: The Big Picture
• [X] Pluralsight: AWS Developer: The Big Picture
• [X] Pluralsight: AWS Networking Deep Dive: Virtual Private Cloud (VPC)
• [X] Pluralsight: AWS VPC Operations
• [X] Pluralsight: Building Applications Using Elastic Beanstalk
• [ ] Servers for Hackers Series
• [ ] The Hacker's Guide to Scaling Python
• [ ] Udacity: HTTP & Web Servers
• [ ] Udacity: Intro to DevOps
• [ ] Udacity: Developing Scalable Apps in Python
• [ ] Udacity: Configuring Linux Web Servers
• [ ] Udacity: Scalable Microservices with Kubernetes
• [X] Udemy: AWS Concepts
• [X] Udemy: Serverless Concepts
• [X] Udemy: AWS Certified Developer - Associate 2018
• [ ] Udacity: Authentication & Authorization: OAuth
• [ ] Udacity: Designing RESTful APIs
• [ ] Udacity: Client-Server Communication
• [X] Youtube: PyConBY 2020: Sebastian Ramirez - Serve ML models easily with FastAPI
• [X] Youtube: FastAPI from the ground up

Be able to perform A/B testing

• [ ] Datacamp: Customer Analytics & A/B Testing in Python
• [ ] Udacity: A/B Testing
• [ ] Udacity: A/B Testing for Business Analysts
• [ ] Youtube: A/B Testing - Simply Explained 0:02:45
• [ ] Youtube: Hypothesis testing with Applications in Data Science 0:10:33

Be able to write unit tests

• [X] Article: Effective testing for machine learning systems
• [X] Datacamp: Unit Testing for Data Science in Python
• [X] Pluralsight: Test-driven Development: The Big Picture
• [ ] Test Driven Development with Python
• [ ] Thoughtbot: Fundamentals of TDD
• [ ] Treehouse: Python Testing
• [ ] Udacity: Software Analysis & Testing
• [ ] Udacity: Software Testing
• [ ] Udacity: Software Debugging

Be proficient in Python

• [ ] Article: No Really, Python's Pathlib is Great
• [X] Book: A Byte of Python
• [X] Book: Learn Python The Hard way
• [ ] Book: Python 201
• [ ] Book: Python Anti-Patterns
• [ ] Book: Real Python
• [ ] Book: The Python 3 Standard Library By Example
• [ ] Book: Writing Idiomatic Python 3
• [X] Codecademy: Learn Python
• [X] Cognitiveclass.ai: Python for Data Science
• [X] Datacamp: Python for R Users
• [X] Datacamp: Python for Spreadsheet Users
• [ ] Datacamp: Python for MATLAB Users
• [X] Datacamp: Importing Data in Python (Part 1)
• [X] Datacamp: Intermediate Python for Data Science
• [X] Datacamp: Python Data Science Toolbox (Part 1)
• [X] Datacamp: Python Data Science Toolbox (Part 2)
• [X] Datacamp: Intro to Python for Finance
• [X] Datacamp: Writing Efficient Python Code
• [X] Datacamp: Writing Functions in Python
• [ ] Datacamp: Working with Dates and Times in Python
• [X] Datacamp: Object-Oriented Programming in Python
• [X] edX: Introduction to Python for Data Science
• [X] edX: Programming with Python for Data Science
• [X] Google's Python Class
• [X] Treehouse: Python Basics
• [ ] Treehouse: Python collections
• [ ] Treehouse: Date and Time
• [ ] Treehouse: CSV And JSON
• [ ] Treehouse: Functional Programming with Python
• [ ] Treehouse: Python Decorators
• [ ] Treehouse: Write Better Python
• [ ] Thoughtbot: Regular Expressions
• [X] TheNewBoston: Python Programming Tutorials
• [ ] Udacity: Introduction to Python Programming
• [ ] Udacity: Programming Foundations with Python
• [ ] Udacity: What is Programming?

Be familiar with compiled languages

• [X] Codecademy: Learn Java
• [ ] Udacity: C++ For Programmers
• [X] Udacity: Java Programming Basics

Have a general understanding of other parts of the stack

• [ ] Book: Refactoring UI
• [X] Codecademy: Learn HTML
• [ ] Codecademy: Learn Color Design
• [X] Codecademy: Learn SASS
• [X] Codecademy: Make a website
• [X] Codecademy: Learn ReactJS: Part I
• [X] Codecademy: Learn ReactJS: Part II
• [X] Codecademy: Learn JavaScript
• [X] Codecademy: Jquery Track
• [X] Codecademy: Learn Ruby
• [X] Code School: Fundamentals of Design
• [X] Code School: Blasting Off with Bootstrap
• [X] (ES6) - Beau teaches JavaScript
• [X] Pluralsight: UX Fundamentals
• [X] Pluralsight: HTML, CSS, and JavaScript: The Big Picture
• [X] Pluralsight: CSS Positioning
• [X] Pluralsight: Introduction to CSS
• [X] Pluralsight: CSS: Specificity, the Box Model, and Best Practices
• [X] Pluralsight: CSS: Using Flexbox for Layout
• [X] Pluralsight: Using The Chrome Developer Tools
• [ ] Thoughtbot: Design for Developers
• [X] Treehouse: HTML
• [X] Treehouse: Javascript Booleans
• [X] Udacity: ES6 - JavaScript Improved
• [X] Udacity: Intro to Javascript
• [X] Udacity: Object Oriented JS 1
• [X] Udacity: Object Oriented JS 2
• [X] Udemy: Understanding Typescript

Be familiar with fundamental Computer Science concepts

• [X] Codecademy: Big O
• [ ] Crashcourse: Computer Science
• [X] Grokking Algorithms
• [ ] Khan Academy: Data Structures
• [ ] Udacity: Intro to Algorithms
• [ ] Udacity: Intro to Computer Science
• [ ] Udacity: Intro to Theoretical Computer Science
• [ ] Udacity: Programming Languages
• [ ] Udacity: Networking for Web Developers

Be able to apply proper software engineering process

• [ ] Launch School: Agile Planning
• [X] Pluralsight: Product Owner Fundamentals
• [X] Pluralsight: Scrum Master Fundamentals - Foundations
• [X] Pluralsight: Security Awareness: Basic Concepts and Terminology
• [X] Pluralsight: Secure Software Development
• [X] Pluralsight: Clean Architecture: Patterns, Practices, and Principles
• [ ] Thoughtbot: Software Development Process
• [ ] Thoughtbot: Refactoring
• [ ] Udacity: Design of Computer Programs
• [ ] Udacity: Product Design
• [ ] Udacity: Rapid Prototyping
• [ ] Udacity: Software Architecture and Design
• [ ] Udacity: Software Development Process
• [ ] Udacity: Full Stack Foundations

Be able to efficiently use a text editor

• [ ] Learn Enough Text Editor to Be Dangerous
• [ ] Mastering Pycharm

Be able to communicate and collaborate well

• [ ] Google: Technical Writing
• [X] Book: Emotional Intelligence
• [X] Book: How to Win Friends & Influence People
• [X] Book: Influence: The Psychology of Persuasion
• [X] Book: Leaders Eat Last: Why Some Teams Pull Together and Others Don't
• [X] Book: Multipliers: How the Best Leaders Make Everyone Smarter
• [X] Book: Soft Skills: The software developer's life manual
• [X] Book: The New One Minute Manager
• [X] Youtube: Building a psychologically safe workplace | Amy Edmondson | TEDxHGSE

Be familiar with the hiring pipeline

• [ ] Datacamp: Preparing for Statistics Interview Questions in Python
• [ ] Datacamp: Preparing for Coding Interview Questions in Python
• [X] Udacity: Optimize your GitHub
• [X] Udacity: Strengthen Your LinkedIn Network & Brand
• [X] Udacity: Data Science Interview Prep
• [X] Udacity: Full-Stack Interview Prep
• [ ] Udacity: Refresh Your Resume
• [ ] Udacity: Craft Your Cover Letter
• [ ] Udacity: Technical Interview
• [ ] Youtube: Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers

Broaden Perspective

• [X] Book: Atomic Habits
• [X] Book: Deep Work
• [X] Book: Outliers: The Story of Success
• [X] Book: Rich Dad Poor Dad
• [X] Book: The Power of Broke
• [X] Book: The 10X Rule
• [X] Book: The Millionaire Fastlane
• [X] Book: The Subtle Art of Not Giving a F**k