How to build your own Neural Network from scratch in Python
A beginner’s guide to understanding the inner workings of Deep Learning
Update: When I wrote this article a year ago, I did not expect it to be this popular. Since then, this article has been viewed more than 450,000 times, with more than 30,000 claps. It has also made it to the front page of Google, and it is among the first few search results for ‘Neural Network’. Many of you have reached out to me, and I am deeply humbled by the impact of this article on your learning journey.
This article also caught the eye of the editors at Packt Publishing. Shortly after this article was published, I was offered to be the sole author of the book Neural Network Projects with Python. Today, I am happy to share with you that my book has been published!
The book is a continuation of this article, and it covers end-to-end implementation of neural network projects in areas such as face recognition, sentiment analysis, noise removal etc. Every chapter features a unique neural network architecture, including Convolutional Neural Networks, Long Short-Term Memory Nets and Siamese Neural Networks. If you’re looking to create a strong machine learning portfolio with deep learning projects, do consider getting the book!
Motivation: As part of my personal journey to gain a better understanding of Deep Learning, I’ve decided to build a Neural Network from scratch without a deep learning library like TensorFlow. I believe that understanding the inner workings of a Neural Network is important to any aspiring Data Scientist.
This article contains what I’ve learned, and hopefully it’ll be useful for you as well!
What’s a Neural Network?
Most introductory texts to Neural Networks brings up brain analogies when describing them. Without delving into brain analogies, I find it easier to simply describe Neural Networks as a mathematical function that maps a given input to a desired…
Deep Learning with Python: Neural Networks (complete tutorial)
Build, Plot & Explain Artificial Neural Networks with TensorFlow
Summary
In this article, I will show how to build Neural Networks with Python and how to explain Deep Learning to the Business using visualization and creating an explainer for model predictions.
Deep Learning is a type of machine learning that imitates the way humans gain certain types of knowledge, and it got more popular over the years compared to standard models. While traditional algorithms are linear, Deep Learning models, generally Neural Networks, are stacked in a hierarchy of increasing complexity and abstraction (therefore the “deep” in Deep Learning).
Neural Networks are based on a collection of connected units (neurons), which, just like the synapses in a brain, can transmit a signal to other neurons, so that, acting like interconnected brain cells, they can learn and make decisions in a more human-like manner.
Today, Deep Learning is so popular that many companies want to use it even though they don’t fully understand it. Often data scientists, first have to simplify these complex algorithms for the Business, and then explain and justify the results of the models, which is not always simple with Neural Networks. I think the best way to do it is through visualization.
I will present some useful Python code that can be easily applied in other similar cases (just copy, paste, run) and walk through every line of code with comments so that you can replicate the examples.
In particular, I will go through:
- Environment Setup, tensorflow vs pytorch.
- Artificial Neural Networks breakdown, input, output, hidden layers, activation functions.
- Deep Learning with deep neural networks.
- Model design with tensorflow/keras.
- Visualization of Neural Networks with python.
- Model training & testing.
- Explainability with shap.
Setup
There are two main libraries for building Neural Networks: TensorFlow (developed by Google) and PyTorch…