Neural Networks Fundamentals with Python – implementing neural networks from scratch
Repository holding the code for the “Neural Networks Fundamentals with Python” book available here.
Ever wondered what a neural network really is, how it works or how to implement one?
Well, I did, and that is why I tried implementing one.
And I failed.
Some years later, I tried again, and it worked!
And it was amazing!
Now, I just want to help you do it yourself, so that you can take a peek
behind the curtains of this world of machine learning, deep learning,
and all those buzzwords.
In this short book, I will take you through the journey of implementing a neural network
from scratch, and then we will use these networks you created to perform
machine learning tasks.
We will also play around with them and so all sorts of really interesting machine learning
and deep learning experiments.
The book explains you the things that are needed, step-by-step,
and gives you a reference implementation that you can find in this repo.
By the time you are done with the book, you will have at your disposal
a simple neural network framework that actually works, and that you have already
used to explore interesting concepts from the worlds of machine learning and
deep learning.
By the way, if you are unsure whether or not this book is for you,
you can always read its chapters for free on my blog.
To make sure you have all dependencies installed (which should be only NumPy, really)
be sure to check you meet the requirements set in the requirements file.
The simplest way to go about doing so would be by running the command
python -m pip install -r requirements.txt