This repo contains all the code from the book Deep Learning From Scratch, published by O'Reilly in September 2019.
It was mostly for me to keep the code I was writing for the book organized, but my hope is readers can clone this repo and step through the code systematically themselves to better understand the concepts.
Each chapter has two notebooks: a Code notebook and a Math notebook. Each Code notebook contains the Python code for corresponding chapter and can be run start to finish to generate the results from the chapters. The Math notebooks were just for me to store the LaTeX equations used in the book, taking advantage of Jupyter's LaTeX rendering functionality.
In the notebooks in the Chapters 4, 5, and 7 folders, I import classes from lincoln, rather than putting those classes in the Jupyter Notebook itself. lincoln is not currently a pip installable library; th way I'd recommend to be able to import it and run these notebooks is to add a line like the following your .bashrc file:
export PYTHONPATH=$PYTHONPATH:/Users/seth/development/DLFS_code/lincolnThis will cause Python to search this path for a module called lincoln when you run the import command (of course, you'll have to replace the path above with the relevant path on your machine once you clone this repo). Then, simply source your .bashrc file before running the jupyter notebook command and you should be good to go.
While I don't spend much time delving into the details in the main text of the book, I have implemented the batch, multi-channel convolution operation in pure Numpy (I do describe how to do this and share the code in the book's Appendix). In this notebook, I demonstrate using this operation to train a single layer CNN from scratch in pure Numpy to get over 90% accuracy on MNIST.