Previous: W1,
Next: W3
# Summary
📗 Examples and quizzes:
E2
📗 Programming homework: part 2 of
P1
📗 No lecture.
📗 Wednesday math homework office hours: 5:00 to 6:00,
Guest Link
📗 Thursday math homework office hours: 5:00 to 6:00 (not the first two weeks)
📗 Friday office hours for other things: 5:00 to 6:00,
Guest Link
# Lectures
📗 Slides
Lecture 3:
Slides.
Lecture 4:
Slides.
📗 Videos
Lecture 3 Part 1 (Neural Network):
Link
Lecture 3 Part 2 (Backpropagation):
Link
Lecture 3 Part 3 (Multi-Layer Network):
Link
Lecture 4 Part 1 (Stochastic Gradient):
Link
Lecture 4 Part 2 (Multi-Class Classification):
Link
Lecture 4 Part 3 (Regularization):
Link
📗 Notes
I recorded a video talking about P1 and how it is graded:
Link. In case you are curious, I explained how the auto-grading scripts (JavaScript) grade P1 and M1. You do not have to watch it to solve P1.
The video series by 3Blue1Brown on Neural Networks are really good:
Playlist.
# Other Materials
📗 Relevant websites
(from week 1) Gradient Descent:
Link
Neural Network:
Link
Neural Network MNIST Visualization:
Link
Neural Network MNIST Demo:
Link
Neural Network Videos by Grant Sanderson:
Playlist (Thanks Dan Drake for the recommendation)
Stochastic Gradient Descent:
Link
Overfitting:
Link
Neural Network Snake:
Link
Neural Network Car:
Link
Neural Network Flappy Bird:
Link
Neural Network Mario:
Link
📗 YouTube videos from 2019
How to construct XOR network?
Link
How derive 2-layer neural network gradient descent step?
Link
How derive multi-layer neural network gradient descent induction step?
Link
Comparison between L1 and L2 regularization.
Link
Example (Quiz): Cross validation accuracy
Link
Last Updated: November 09, 2021 at 12:30 AM