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