Neural Networks In Python From Scratch. Build step by step!

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

4.76 (403 reviews)

Udemy

platform

English

language

Data Science

What you will 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

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Comidoc Review

Our Verdict

Neural Networks In Python From Scratch stands out as a valuable resource for those looking to build neural networks from the ground up using Python. Although there is room for improvement in terms of explanations of some Python-specific concepts and parameter context, the course provides a solid foundation in understanding neural networks. It's definitely worth considering if you're keen on mastering this complex topic.

What We Liked

Comprehensive course covering the basics of neural networks using Python from scratch
Concise and clear explanations allowing for understanding of complex concepts
Well-chosen examples, helping to illustrate theoretical principles
Q&A section answers are helpful and informative

Potential Drawbacks

Lack of in-depth explanation of some Python code may be challenging for beginners
Matrix manipulations could benefit from additional visualization or explanation
Could provide more context about some parameters, like learning rates
Some repetition required to fully grasp certain topics