Neural Networks in Python from Scratch: Learning by Doing

Understand machine learning and deep learning by building linear regression and gradient descent from the ground up.

Created by: Loek van den Ouweland
Passionate Python Teacher

What Will I Learn?

• The basic functions for any neural network, by coding linear regression, cost functions and back propagation
• Understand the properties of neural networks by adjusting learning rates and biases
• Train a network by implementing a gradient descent algorithm
• Normalizing inputs for multi-input networks
• Create classification networks by implementing multiple output neurons and activation
• Improve network accuracy by implementing hidden layers for non-linear data

Requirements

• You have an interest in neural networks.
• You have some programming experience in Python or another language.

Target audience

• Developer who want to learn the mechanics of neural networks
• Developers who want to avoid using neural network libraries and frameworks
• Developers who use frameworks but want to learn the meaning of the individual network parameters