[Day 122] Dive into Deep Learning - Interactive deep learning book with code, math, and discussions

 Hello :)
Today is Day 122!


A quick summary of today:


I randomly found the book from some post on r/learnmachinelearning about books on DL (I always manage to find some new cool book from these weekly posts)

It seems a bit crazy that such a book is available for free. It is adopted by so many university courses and so many great practitioners participated in its writing. 
Every topic contains an executable colab linked to it, and its available in PyTorch, Tensorflow, MXNET and Jax. 

Today I had a read through 3. Linear Neural Networks for Regression

4. Linear Neural Networks for Classification

5. Multilayer Perceptrons

6. Builders’ Guide
The book is great because under each topic there is also a live discussion section where one can ask questions.

Overall (so far!), I think An Introduction to Statistical Learning is better. It has code as well and very detailed exercises. The problem I encountered in the above 4 chapters is that sometimes my browser did not read the math notations, so I had to open the page in colab in order to see the proper math notations. The explanations were fine, but maybe I just hold ISL very high in my list haha. 

Dive into DL has so many chapters, but next I am planning to read (and maybe take notes of:)
12. Optimization Algorithms (because it dives into the details of each optimizer)

21. Recommender Systems (seeing some of the theory from XCS224W:ML with Graphs in practice, and from another point of view)


22. Apendix: Math for DL (will read through it)


Tomorrow evening the MLx Fundamentals organised by Global AI for good and Oxford University so I am very excited. 
The full schedule for day 1 (out of 4) is:


That is all for today!
See you tomorrow :)

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