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Machine Learning, Scholarly, Tutorial
Neural Networks from Scratch with Python Code and Math in Detail— I
Building neural networks from scratch. From the math behind them to step-by-step implementation coding samples in Python with Google Colab
Author(s): Pratik Shukla, Roberto Iriondo
Last updated December 1, 2021
Note: In our second tutorial on neural networks, we dive in-depth into the limitations and advantages of using neural networks. We show how to implement neural nets with hidden layers and how these lead to a higher accuracy rate on our predictions, along with implementation samples in Python on Google Colab.
What is a neural network?
Neural networks form the base of deep learning, which is a subfield of machine learning, where the structure of the human brain inspires the algorithms. Neural networks take input data, train themselves to recognize patterns found in the data, and then predict the output for a new set of similar data. Therefore, a neural network can be thought of as the functional unit of deep learning, which mimics the behavior of the human brain to solve complex data-driven problems.
The first thing that comes to our mind when we think of “neural networks” is biology, and indeed, neural nets are inspired by our brains.
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Let’s try to understand them:
