Please use this thread to discuss lesson 8. Since this is Part 2, feel free to ask more advanced or slightly tangential questions  although if your question is not related to the lesson much at all, please use a different topic.
Thread for general chit chat (we won’t be monitoring this).
Note that this is a forum wiki thread, so you all can edit this post to add/change/organize info to help make it better! To edit, click on the little edit icon at the bottom of this post. Here’s a pic of what to look for:
Lesson resources
 Edited lesson video
 Slides
 Course notebooks

Excel spreadsheets (today’s is called
broadcasting.xlsx
). There’s also a Google Sheet version thanks to @Moody  Use this Jupyter notebook to start running the “Deep Learning From the Foundations” notebooks on Colab
 Ensure your fastai lib is up to date
 You’ll also need to:
conda install nbconvert
 You’ll also need to:
conda install fire c condaforge
 Notes thread
Errata
Sometimes, occasionally, shockingly, Jeremy makes mistakes. It is rumored that these mistakes were made in this lesson:
 Jeremy claimed that these are equivalent:
for i in range(ar):
c[i] = (a[i].unsqueeze(1)*b).sum(dim=0)
c[i] = (a[i,None]*b).sum(dim=0)
But they’re not (noticed by @stas  thanks!) The 2nd one isn’t indexing the second axis. So it should be:
for i in range(ar):
c[i] = (a[i,:,None]*b).sum(dim=0)
Things mentioned in the lesson
 Jeremy’s blog posts about Swift: fast.ai Embracing Swift for Deep Learning and High Performance Numeric Programming with Swift: Explorations and Reflections
 Rachel’s post on starting to blog: Why you (yes, you) should blog
 Numpy docs on broadcasting
 Numpy docs on einsum (has lots of great examples)
 The Matrix Calculus You Need For Deep Learning by Terence Parr and Jeremy
 Detexify (for finding math symbols) and Wikipedia list of symbols
 The matrix multiplication song
 Thread by @pierreguillou seeking feedback on best practices for study groups
Papers
 Delving Deep into Rectifiers: Surpassing HumanLevel Performance on ImageNet Classification– 2015 paper that won ImageNet, and introduced ResNet and Kaiming Initialization.
 Understanding the difficulty of training deep feedforward neural networks– paper that introduced Xavier initialization
 Fixup Initialization: Residual Learning Without Normalization – paper highlighting importance of normalisation  training 10,000 layer network without regularisation
Other helpful resources
 Jake VanderPlas’ explanation of broadcasting
 Mathpix  turns images into LaTeX

Tutorial by @jeffhale on "How to use
if __name__=='__main__'
"  Khan academy lesson on the chain rule
 Basic PyTorch Tensor Tutorial (Includes Jupyter Notebook)
 Xavier Initialisation (why divide over sqrt(M))  Link to a blog post that explains it nicely
 Xavier and Kaiming initialisation : Link to a blog post that explains the two papers, and in particular the math in detail
“Assigned” Homework
 Review concepts 16 concepts from Course 1 (lessons 1  7): Affine Functions & nonlinearities; Parameters & activations; Random initialization & transfer learning; SGD Momentum Adam (not sure what this one is, Jeremy’s head covers it in the video); Convolutions; Batchnorm; Dropout; Data augmentation; Weight decay; Res/dense blocks; Image classification and regression; Embeddings; Continuous & Categorical variables; Collaborative filtering; Language models; NLP classification; Segmentation; Unet; GANS
 Make sure you understand broadcasting
 Read section 2.2 in Delving Deep into Rectifiers: Surpassing HumanLevel Performance on ImageNet Classification
 Try to replicate as much of the notebooks as you can without peeking; when you get stuck, peek at the lesson notebook, but then close it and try to do it yourself