Learning Philosophy:
- The Power of Tiny Gains
- Master Adjacent Disciplines
- T-shaped skills
- Data Scientists Should Be More End-to-End
- Just in Time Learning
- Book: Delivering Happiness
- Book: Good to Great: Why Some Companies Make the Leap...And Others Don't
- Book: Hello, Startup: A Programmer's Guide to Building Products, Technologies, and Teams
- Book: How Google Works
- Book: Learn to Earn: A Beginner's Guide to the Basics of Investing and Business
- Book: Rework
- Book: The Airbnb Story
- Book: The Personal MBA
- Facebook: Digital marketing: get started
- Facebook: Digital marketing: go further
- Google Analytics for Beginners
- Google: Fundamentals of Digital Marketing
- Moz: The Beginner's Guide to SEO
- Smartly: Marketing Fundamentals
- Treehouse: SEO Basics
- Udacity: App Monetization
- Udacity: App Marketing
- Udacity: Get Your Startup Started
- Udacity: How to Build a Startup
- Youtube: SEO Unlocked
- Welcome to the SEO Unlocked
0:10:09
- 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
- Welcome to the SEO Unlocked
- AWS: Types of Machine Learning Solutions
- Article: Apply Machine Learning to your Business
- Article: Resilience and Vibrancy: The 2020 Data & AI Landscape
- Book: AI Superpowers: China, Silicon Valley, and the New World Order
- Book: A Human's Guide to Machine Intelligence
- Book: The Future Computed
- Book: Machine Learning Yearning by Andrew Ng
- Book: Prediction Machines: The Simple Economics of Artificial Intelligence
- Book: Building Machine Learning Powered Applications: Going from Idea to Product
- Coursera: AI For Everyone
- Datacamp: Case Studies in Statistical Thinking
- Datacamp: Data Science for Everyone
- Datacamp: Machine Learning with the Experts: School Budgets
- Datacamp: Machine Learning for Everyone
- Datacamp: Analyzing Police Activity with pandas
- Datacamp: HR Analytics in Python: Predicting Employee Churn
- Datacamp: Predicting Customer Churn in Python
- Datacamp: Data Science for Managers
- Facebook: Field Guide to Machine Learning
- 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
- Pluralsight: How to Think About Machine Learning Algorithms
- Udacity: Problem Solving with Advanced Analytics
- Youtube: Vincent Warmerdam: The profession of solving (the wrong problem) | PyData Amsterdam 2019
- Youtube: Making Money from AI by Predicting Sales - Jay's Intro to AI Part 2
- Youtube: How does YouTube recommend videos? - AI EXPLAINED!
0:33:53
- Youtube: How does Google Translate's AI work?
0:15:02
- Youtube: Data Science in Finance
0:17:52
- Youtube: The Age of AI
- How Far is Too Far? | The Age of A.I.
0:34:39
- Healed through A.I. | The Age of A.I.
0:39:55
- Using A.I. to build a better human | The Age of A.I.
0:44:27
- Love, art and stories: decoded | The Age of A.I.
0:38:57
- The 'Space Architects' of Mars | The Age of A.I.
0:30:10
- Will a robot take my job? | The Age of A.I.
0:36:14
- Saving the world one algorithm at a time | The Age of A.I.
0:46:37
- How A.I. is searching for Aliens | The Age of A.I.
0:36:12
- How Far is Too Far? | The Age of A.I.
- Youtube: Gradient Dissent Podcast
- DeepChem creator Bharath Ramsundar on using deep learning for molecules and medicine discovery
0:55:11
- ML Research and Production Pipelines with Chip Huyen
0:43:07
- Product Management for AI with Peter Skomoroch
1:28:14
- 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
- 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
- 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
- DeepChem creator Bharath Ramsundar on using deep learning for molecules and medicine discovery
- Youtube: Accuracy as a Failure
- 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
- Article: Weak Supervision for Online Discussions
- Youtube: Snorkel: Dark Data and Machine Learning - Christopher Ré
- Youtube: Training a NER Model with Prodigy and Transfer Learning
- Youtube: Training a New Entity Type with Prodigy – annotation powered by active learning
- Youtube: ECCV 2020 WSL tutorial: 4. Human-in-the-loop annotations
- Datacamp: Intro to Python for Data Science
- Pluralsight: Working with Multidimensional Data Using NumPy
- Datacamp: pandas Foundations
- Datacamp: Pandas Joins for Spreadsheet Users
- 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
- edX: Implementing Predictive Analytics with Spark in Azure HDInsight
- Article: Modern Pandas
- 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
- edX: Analyzing and Visualizing Data with Excel
- Codecademy: SQL Track
- 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
- 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
- Codecademy: Learn the Command Line
- 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
- Udacity: Linux Command Line Basics
- Udacity: Linux Web Servers
- Udacity: Shell Workshop
- Udacity: Web Tooling & Automation
- Web Bos: Command Line Power User
- 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
- Datacamp: Dimensionality Reduction in Python
- Datacamp: Preprocessing for Machine Learning in Python
- Datacamp: Data Types for Data Science
- Datacamp: Cleaning Data in Python
- 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
- edX: Data Science Essentials
- Google: Feature Engineering
- Udacity: Creating an Analytical Dataset
- Pluralsight: Getting Started with Jupyter Notebook and Python
- Youtube: William Horton - A Brief History of Jupyter Notebooks
- Datacamp: Introduction to Data Visualization with Python
- Datacamp: Introduction to Seaborn
- 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
- Paper: A Neural Probabilistic Language Model
- Paper: Efficient Estimation of Word Representations in Vector Space
- Paper: Sequence to Sequence Learning with Neural Networks
- Paper: Neural Machine Translation by Jointly Learning to Align and Translate
- Paper: Attention Is All You Need
- Paper: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- Paper: XLNet: Generalized Autoregressive Pretraining for Language Understanding
- 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
- Paper: Amazon.com Recommendations Item-to-Item Collaborative Filtering
- Paper: Collaborative Filtering for Implicit Feedback Datasets
- Paper: BPR: Bayesian Personalized Ranking from Implicit Feedback
- Paper: Factorization Machines
- Paper: Wide & Deep Learning for Recommender Systems
- Paper: Neural Factorization Machines for Sparse Predictive Analytics
- Paper: Multiword Expressions: A Pain in the Neck for NLP
- Paper: PyTorch: An Imperative Style, High-Performance Deep Learning Library
- Paper: ALBERT: A LITE BERT FOR SELF-SUPERVISED LEARNING OF LANGUAGE REPRESENTATIONS
- Paper: Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
- Paper: A Simple Framework for Contrastive Learning of Visual Representations
- Paper: Self-Supervised Learning of Pretext-Invariant Representations
- Paper: FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
- Paper: Self-Labelling via Simultalaneous Clustering and Representation Learning
- Paper: A Survey on Contextual Embeddings
- Paper: A survey on Semi-, Self- and Unsupervised Techniques in Image Classification
- Paper: Shortcut Learning in Deep Neural Networks
- Paper: Multi-document Summarization by using TextRank and Maximal Marginal Relevance for Text in Bahasa Indonesia
- Paper: Train Once, Test Anywhere: Zero-Shot Learning for Text Classification
- Paper: Zero-shot Text Classification With Generative Language Models
- Paper: How to Fine-Tune BERT for Text Classification?
- Paper: Universal Sentence Encoder
- Paper: Enriching Word Vectors with Subword Information
- Paper: Deep Learning Based Text Classification: A Comprehensive Review
- Paper: Beyond Accuracy: Behavioral Testing of NLP models with CheckList
- Paper: Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks
- Paper: Temporal Ensembling for Semi-Supervised Learning
- Paper: Boosting Self-Supervised Learning via Knowledge Transfer
- Paper: Follow-up Question Generation
- Paper: The Hardware Lottery
- Paper: Question Generation via Overgenerating Transformations and Ranking
- Paper: Good Question! Statistical Ranking for Question Generation
- Paper: Interpolation Consistency Training for Semi-Supervised Learning
- 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
- The Essence of Calculus, Chapter 1
- 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
- Vectors, what even are they? | Essence of linear algebra, chapter 1
- 3Blue1Brown: Neural networks
- 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
- 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)
- 1. The Geometry of Linear Equations
0:39:49
- 2. Elimination with Matrices.
0:47:41
- 3. Multiplication and Inverse Matrices
0:46:48
- 4. Factorization into A = LU
0:48:05
- 5. Transposes, Permutations, Spaces R^n
0:47:41
- 6. Column Space and Nullspace
0:46:01
- 9. Independence, Basis, and Dimension
0:50:14
- 10. The Four Fundamental Subspaces
0:49:20
- 11. Matrix Spaces; Rank 1; Small World Graphs
0:45:55
- 14. Orthogonal Vectors and Subspaces
0:49:47
- 15. Projections onto Subspaces
0:48:51
- 16. Projection Matrices and Least Squares
0:48:05
- 17. Orthogonal Matrices and Gram-Schmidt
0:49:09
- 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
- 1. The Geometry of Linear Equations
- 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
- 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
- StatQuest: Histograms, Clearly Explained
- Udacity: Algebra Review
- Udacity: Differential Equations in Action
- 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
- Youtube: Visualizing Deep Learning
- Article: I trained a model. What is next?
- Article: Organizing machine learning projects: project management guidelines
- Article: Building machine learning products: a problem well-defined is a problem half-solved.
- Article: Simple considerations for simple people building fancy neural networks
- 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
- Coursera: Structuring Machine Learning Projects
- Datacamp: Conda Essentials
- Datacamp: Conda for Building & Distributing Packages
- Datacamp: Creating Robust Python Workflows
- Datacamp: Software Engineering for Data Scientists in Python
- Datacamp: Designing Machine Learning Workflows in Python
- Datacamp: Object-Oriented Programming in Python
- Datacamp: Command Line Automation in Python
- Datacamp: Introduction to Data Engineering
- Datacamp: Experimental Design in Python
- Full Stack Deep Learning Bootcamp: March 2019
- Lecture 1: Introduction to Deep Learning
- Lecture 2: Setting Up Machine Learning Projects
- Lecture 3: Introduction to the Text Recognizer Project
- Lecture 4: Infrastructure and Tooling
- Lecture 5: Tracking Experiments
- Lecture 6: Data Management
- Lecture 7: Machine Learning Teams
- Lecture 9: Lukas Biewald
- Lecture 10: Troubleshooting Deep Neural Networks
- Lecture 11: Labs 6-9: Detection, Data Labeling, Testing and Deployment
- Lecture 12: Testing and Deployment
- Lecture 13: Research Directions
- Lecture 14: Jeremy Howard
- Lecture 15: Richard Socher
- 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
- Lecture 1: Course Overview + The Shell (2020)
- Treehouse: Object Oriented Python
- Treehouse: Setup Local Python Environment
- Udacity: Writing READMEs
- Youtube: Weights and Biases Tutorial
- Youtube: MLOps Tutorials
- Article: Mastering Git Stash Workflow
- Codecademy: Learn Git
- Code School: Git Real
- Datacamp: Introduction to Git for Data Science
- Learn enough git to be dangerous
- Thoughtbot: Mastering Git
- Udacity: GitHub & Collaboration
- Udacity: How to Use Git and GitHub
- Udacity: Version Control with Git
- 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: Hacker's Guide to Fundamental Machine Learning Algorithms with Python
- AWS: The Elements of Data Science
- Datacamp: AI Fundamentals
- Datacamp: Extreme Gradient Boosting with XGBoost
- Datacamp: Ensemble Methods in Python
- Datacamp: Foundations of Predictive Analytics in Python (Part 1)
- Datacamp: Foundations of Predictive Analytics in Python (Part 2)
- Elements of AI
- edX: Principles of Machine Learning
- edX: Data Science Essentials
- StatQuest: Machine Learning
- A Gentle Introduction to Machine Learning
0:12:45
- Machine Learning Fundamentals: Cross Validation
0:06:04
- Machine Learning Fundamentals: The Confusion Matrix
0:07:12
- Machine Learning Fundamentals: Sensitivity and Specificity
0:11:46
- 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
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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
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0:20:16
- Decision Trees in Python from Start to Finish
1:06:23
- A Gentle Introduction to Machine Learning
- Udacity: A Friendly Introduction to Machine Learning
- Udacity: Intro to Data Analysis
- Udacity: Intro to Data Science
- Udacity: Intro to Machine Learning
- Udacity: Classification Models
- Article: The Last 5 Years In Deep Learning
- 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: 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: Deep Learning Algorithms - The Complete Guide
- AWS: Understanding Neural Networks
- Book: Pattern Recognition and Machine Learning
- Coursera: Neural Networks and Deep Learning
- 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
- 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
- DeepMind x UCL | Deep Learning Lectures | 1/12 | Intro to Machine Learning & AI
- Fast.ai: Deep Learning for Coder (2020)
- 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
- MIT Introduction to Deep Learning | 6.S191
- Pluralsight: Deep Learning: The Big Picture
- Udacity: Deep Learning
- 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
- Neural Networks from Scratch - P.1 Intro and Neuron Code
- Youtube: Visualizing Deep Learning
- Youtube: Deep Double Descent
- Youtube: How do we check if a neural network has learned a specific phenomenon?
- Article: Label Smoothing Explained using Microsoft Excel
- Youtube: What is Adversarial Machine Learning and what to do about it? – Adversarial example compilation
- Datacamp: Supervised Learning with scikit-learn
- Datacamp: Machine Learning with Tree-Based Models in Python
- Datacamp: Introduction to Linear Modeling in Python
- Datacamp: Linear Classifiers in Python
- Datacamp: Generalized Linear Models in Python
- 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
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- Coursera: Convolutional Neural Networks in TensorFlow
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- Book: Deep Learning with Python (Page: 276)
- Datacamp: Deep Learning in Python
- Datacamp: Convolutional Neural Networks for Image Processing
- Datacamp: Introduction to TensorFlow in Python
- Datacamp: Introduction to Deep Learning with Keras
- Datacamp: Advanced Deep Learning with Keras
- Google: Intro to Tensorflow
- Google: Machine Learning Crash Course
- Pluralsight: Deep Learning with Keras
- Udacity: Intro to TensorFlow for Deep Learning
- Article: An introduction to PyTorch Lightning with comparisons to PyTorch
- Article: Scaling Logistic Regression Via Multi-GPU/TPU Training
- Datacamp: Introduction to Deep Learning with PyTorch
- Deeplizard: Neural Network Programming - Deep Learning with PyTorch
- Udacity: Intro to Deep Learning with PyTorch
- Youtube: PyTorch Lightning 101
- Youtube: SimCLR with PyTorch Lightning
- Youtube: PyTorch Performance Tuning Guide
26:41:00
- Youtube: Skin Cancer Detection with PyTorch
- AWS: Amazon Transcribe Deep Dive: Using Feedback Loops to Improve Confidence Level of Transcription
- AWS: Build a Text Classification Model with AWS Glue and Amazon SageMaker
- AWS: Deep Dive on Amazon Rekognition: Building Computer Visions Based Smart Applications
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- AWS: Introduction to Amazon Comprehend
- AWS: Introduction to Amazon Comprehend Medical
- AWS: Introduction to Amazon Elastic Inference
- AWS: Introduction to Amazon Forecast
- AWS: Introduction to Amazon Lex
- AWS: Introduction to Amazon Personalize
- AWS: Introduction to Amazon Polly
- AWS: Introduction to Amazon SageMaker Ground Truth
- AWS: Introduction to Amazon SageMaker Neo
- AWS: Introduction to Amazon Transcribe
- AWS: Introduction to Amazon Translate
- AWS: Introduction to AWS Marketplace - Machine Learning Category
- AWS: Machine Learning Exam Basics
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- Article: From Research to Production with Deep Semi-Supervised Learning
- Article: RecSys 2020 - Takeaways and Notable Papers
- Article: Grouping data points with k-means clustering
- Article: Soft clustering with Gaussian mixed models (EM)
- Article: Introduction to autoencoders
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1:10:02
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2:27:23
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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
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2:09:14
- Lecture 7 Self-Supervised Learning -- UC Berkeley Spring 2020 - CS294-158 Deep Unsupervised Learning
2:20:41
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0:41:51
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2:16:00
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3:09:49
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2:38:19
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2:01:56
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- Datacamp: Customer Segmentation in Python
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- DeepMind: Inefficient Data Efficiency
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- Youtube: BYOL: Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning (Paper Explained)
- Youtube: A critical analysis of self-supervision, or what we can learn from a single image (Paper Explained)
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- Youtube: CVPR 2020 Tutorial: Towards Annotation-Efficient Learning
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- Youtube: Variational Autoencoders - EXPLAINED!
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- Deep Learning: Unsupervised Learning - Part 2
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0:20:13
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- Learning and Transferring Visual Representations with Few Labels - Carl Doersch
0:32:53
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0:36:31
- Representation Learning beyond Instance Discrimination and Semantic Categorization - Stella Yu
0:43:09
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0:38:06
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0:41:56
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- Youtube: Marco Cuturi - A Primer on Optimal Transport
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- Coursera: Sequence Models
- Coursera: Natural Language Processing in TensorFlow
- CMU: Low-resource NLP Bootcamp 2020
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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 Low resource NLP Bootcamp 2020 (1): NLP Tasks
- CMU: Neural Nets for NLP 2020
- 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 (6): Conditioned Generation
1:07:13
- 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 Neural Nets for NLP 2020 (1): Introduction
- CMU Multilingual NLP 2020
- Datacamp: Advanced NLP with spaCy
- Datacamp: Building Chatbots in Python
- Datacamp: Clustering Methods with SciPy
- Datacamp: Feature Engineering for NLP in Python
- Datacamp: Machine Translation in Python
- Datacamp: Natural Language Processing Fundamentals in Python
- Datacamp: Natural Language Generation in Python
- Datacamp: RNN for Language Modeling
- Datacamp: Regular Expressions in Python
- Datacamp: Sentiment Analysis in Python
- Datacamp: Spoken Language Processing in Python
- RNN and LSTM
- Spacy Tutorial
- Stanford CS224U: Natural Language Understanding | Spring 2019
- 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
- Lecture 1 – Course Overview | Stanford CS224U: Natural Language Understanding | Spring 2019
- Stanford CS224N: Stanford CS224N: NLP with Deep Learning | Winter 2019
- 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
- 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
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- NLP Tutorial With TextBlob and Python - Parts of Speech Tagging
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- NLP Tutorial With TextBlob & Python - Lemmatizating
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- 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
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- Natural Language Processing Tutorial With TextBlob -Tokens,Translation and Ngrams
- Treehouse: Regular expression
- Youtube: fast.ai Code-First Intro to Natural Language Processing
- What is NLP? (NLP video 1)
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- Topic Modeling with SVD & NMF (NLP video 2)
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- Topic Modeling & SVD revisited (NLP video 3)
0:33:05
- 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
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- What is NLP? (NLP video 1)
- NLP Course | For You
- Youtube: BERT Research Series
- YouTube: Intro to NLP with Spacy
- Talk: Practical NLP for the Real World
- YouTube: Level 3 AI Assistant Conference 2020
- Youtube: Conversation Analysis Theory in Chatbots | Michael Szul
- Youtube: Designing Practical NLP Solutions | Ines Montani
- Youtube: Effective Copywriting for Chatbots | Hans Van Dam
- Youtube: Distilling BERT | Sam Sucik
- Youtube: Transformer Policies that improve Dialogues: A Live Demo by Vincent Warmerdam
- Youtube: From Research to Production – Our Process at Rasa | Tanja Bunk
- Youtube: Keynote: Perspective on the 5 Levels of Conversational AI | Alan Nichol
- Youtube: RASA Algorithm Whiteboard
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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
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0:13:48
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0:19:24
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0:19:12
- Rasa Algorithm Whiteboard - Embeddings 4: Whatlies
0:14:03
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0:14:32
- Rasa Algorithm Whiteboard - Attention 2: Keys, Values, Queries
0:12:26
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0:10:55
- Rasa Algorithm Whiteboard: Attention 4 - Transformers
0:14:34
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0:11:46
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0:16:10
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0:14:54
- Rasa Algorithm Whiteboard - Response Selection
0:12:07
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0:09:25
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0:13:32
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0:11:58
- Rasa Algorithm Whiteboard - Implementation of Subword Embeddings
0:10:01
- Rasa Algorithm Whiteboard - BytePair Embeddings
0:12:44
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- CS231n Winter 2016: Lecture 11: ConvNets in practice
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- CS231n Winter 2016: Lecture 12: Deep Learning libraries
1:21:06
- CS231n Winter 2016: Lecture 14: Videos and Unsupervised Learning
1:17:36
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0:19:20
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0:05:51
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- Pluralsight: UX Fundamentals
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- Thoughtbot: Design for Developers
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- Khan Academy: Data Structures
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- Pluralsight: Security Awareness: Basic Concepts and Terminology
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- Pluralsight: Clean Architecture: Patterns, Practices, and Principles
- Thoughtbot: Software Development Process
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- Book: Emotional Intelligence
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- Book: Leaders Eat Last: Why Some Teams Pull Together and Others Don't
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- Youtube: Stanford CS230: Deep Learning | Autumn 2018 | Lecture 8 - Career Advice / Reading Research Papers
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