Gradient Descent, Step-by-Step

A quick explainer for Machine Learning (Stanford CS229) by StatQuest with Josh Starmer. 24 min. Watch on YouTube

Course: Machine Learning (Stanford CS229) (Computer Science). Covers: machine learning supervised learning regression classification svm neural networks gradient descent.

All lectures in Machine Learning (Stanford CS229)

  1. Stanford CS229: Machine Learning Lecture 1 - Andrew Ng (Autumn 2018) · 1 h 15 min
  2. Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018) · 1 h 18 min
  3. Locally Weighted & Logistic Regression | Stanford CS229: Machine Learning - Lecture 3 (Autumn 2018) · 1 h 20 min
  4. Lecture 4 - Perceptron & Generalized Linear Model | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 22 min
  5. Lecture 5 - GDA & Naive Bayes | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018) · 1 h 19 min
  6. Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018) · 1 h 21 min
  7. Lecture 7 - Kernels | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018) · 1 h 20 min
  8. Lecture 8 - Data Splits, Models & Cross-Validation | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 23 min
  9. Discussion Section: Learning Theory | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 26 min
  10. Lecture 9 - Decision Trees and Ensemble Methods | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 21 min
  11. Lecture 10 - Introduction to Neural Networks | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 20 min
  12. Lecture 11 - Backprop & Improving Neural Networks | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 17 min
  13. Lecture 12 - Debugging ML Models and Error Analysis | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 19 min
  14. Lecture 13 - Expectation-Maximization Algorithms | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 21 min
  15. Lecture 14 - EM Algorithm & Factor Analysis | Stanford CS229: Machine Learning Andrew Ng -Autumn2018 · 1 h 20 min
  16. Lecture 15 - PCA and ICA | Stanford CS229: Machine Learning Andrew Ng - Autumn 2018 · 1 h 19 min
  17. Lecture 16 - Independent Component Analysis & RL | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 18 min
  18. Lecture 17 - MDPs & Value/Policy Iteration | Stanford CS229: Machine Learning Andrew Ng (Autumn2018) · 1 h 19 min
  19. Lecture 18 - Continous State MDP & Model Simulation | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 20 min
  20. Lecture 19 - Reward Model & Linear Dynamical System | Stanford CS229: Machine Learning (Autumn 2018) · 1 h 21 min
  21. RL Debugging and Diagnostics | Stanford CS229: Machine Learning Andrew Ng - Lecture 20 (Autumn 2018) · 1 h 13 min