Reading 09: Feedforward Neural Networks + Backpropagation + Review 02 + Exam 02

Readings

• • Slides
• • FCA - Section 8.1 (Until 8.1.1)
• • PML - Section 13.2
• • ISLP - Sections 10.1, 10.2
• • D2L - Sections 5.1 and 5.2
• • UML - Sections 20.1 - 20.5
• • Neural Networks (HdM)
• • Blank Slides

Videos

• • Panopto (ML_to_DL_Lecture01)
• • Harvard CS50 (0:00 - 16:00)
• • Stanford CS221
• • MIT 6.034
• • Neural Networks (StatQuest)
• • pytorch (StatQuest)
• • Matrix Math (StatQuest)
• But what is a neural network? | Deep learning chapter 1

Notebooks

• • Simple FFN Visualization
• • MLP from scratch (rasbt)
• • Tom Notebook

Blogposts

• • Neural Networks (Zhou)
• • Softmax (Zhou)
• • One-hot (Zhou)
• • Loss Functions (Raschka)

Readings

• • Slides
• • FCA - Sections 8.1.1 and 8.2
• • PML - Section 13.3
• • ISLP - Section 10.7
• • D2L - Section 5.3
• • UML - Section 20.6

Videos

• • Panopto (ML_to_DL_Lecture02)
• • Harvard CS50 (16:00 - 39:00)
• • Stanford CS221 - SGD
• • Stanford CS221 - Backprop
• • MIT 6.034
• • SGD (StatQuest)
• • Chain Rule (StatQuest)
• • Backprop (StatQuest)
• • Building Micograd (Karpathy)
• Gradient descent, how neural networks learn | Deep Learning Chapter 2
• Backpropagation, intuitively | Deep Learning Chapter 3
• • Backpropagation calculus | Deep Learning Chapter 4
• • ML to DL Lecture2.5 Video (Tom)

Notebooks

• • Numpy Backprop
• • Pytorch Computation Graphs (rasbt)

Blogposts

• • Backprop (Olah)
• • Backprop (Karpathy)

Readings

• • Slides

Videos

• • Panopto

Notebooks

Readings

Videos

Notebooks