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| Episode | Date |
|---|---|
|
01 - Stanford CS229: Machine Learning Course
|
Jul 13, 2022 |
|
02 - Linear Regression & Gradient Descent
|
Jul 13, 2022 |
|
03 - Locally Weighted & Logistic Regression
|
Jul 13, 2022 |
|
04 - Perceptron & Generalized Linear Model
|
Jul 13, 2022 |
|
05 - GDA & Naive Bayes
|
Jul 13, 2022 |
|
06 - Support Vector Machines
|
Jul 13, 2022 |
|
07 - Kernels
|
Jul 13, 2022 |
|
08 - Data Splits, Models & Cross-Validation
|
Jul 13, 2022 |
|
09 - Approx/Estimation Error & ERM
|
Jul 13, 2022 |
|
10 - Decision Trees & Ensemble Methods
|
Jul 13, 2022 |
|
11 - Introduction to Neural Networks
|
Jul 13, 2022 |
|
12 - Backprop & Improving Neural Networks
|
Jul 13, 2022 |
|
13 - Debugging ML Models & Error Analysis
|
Jul 13, 2022 |
|
14 - Expectation-Maximization Algorithms
|
Jul 13, 2022 |
|
15 - EM Algorithm & Factor Analysis
|
Jul 13, 2022 |
|
16 - Independent Component Analysis & RL
|
Jul 13, 2022 |
|
17 - MDPs & Value/Policy Iteration
|
Jul 13, 2022 |
|
18 - Continuous State MDP & Model Simulation
|
Jul 13, 2022 |
|
19 - Reward Model and Linear Dynamical Systems
|
Jul 13, 2022 |
|
20 - RL Debugging and Diagnostics
|
Jul 13, 2022 |
|
CS 229 - Lec. 12
|
Dec 06, 2019 |
|
CS 229 - Lec. 11
|
Dec 06, 2019 |
|
CS 229 - Lec. 10
|
Dec 06, 2019 |
|
CS 229 - Lec. 9
|
Dec 06, 2019 |
|
CS 229 - Lec. 8
|
Dec 06, 2019 |
|
CS 229 - Lec. 7
|
Dec 06, 2019 |
|
CS 229 - Lec. 6
|
Dec 06, 2019 |
|
CS 229 - Lec. 5
|
Dec 06, 2019 |
|
CS 229 - Lec. 1
|
Dec 06, 2019 |
|
CS229 - Lec. 2
|
Dec 05, 2019 |
|
CS229 - Lec. 4
|
Dec 05, 2019 |
|
CS229 - Lec. 3
|
Dec 05, 2019 |