intermediate8 Bytes15m / Byte

How Neural Networks Really Work

This course explains how neural networks function at a low level, covering backpropagation, activation functions, and gradient descent. Learn to trace data flow and optimise network parameters. After this course, you will be able to build and debug custom neural networks using TensorFlow.

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Lessons
8
Price
Free
Course Curriculum

What you'll learn

4 Modules
8 Lessons
~40m total
01
01
Module 01
Core Principles
This module introduces the fundamental principles behind neural networks, including neurons, layers, forward propagation, and essential activation functions. Understanding these concepts is the foundation for building and training effective networks.
2 lessons10 min
0%
▾
02
02
Module 02
Network Training
This module covers the core algorithms for training neural networks, including backpropagation and gradient descent. Learn how networks learn from data and optimise their parameters to achieve high accuracy.
2 lessons10 min🔒 Locked
0%
▾
03
03
Module 03
Backpropagation Fundamentals
This module explores the core principles of backpropagation, the algorithm that drives learning in neural networks. We'll cover gradient descent, chain rule, and how errors are propagated through the network's layers.
2 lessons10 min🔒 Locked
0%
▾
04
04
Module 04
Optimisation Techniques
Learn advanced optimisation techniques to improve neural network performance. This module covers activation functions, regularization, and hyperparameter tuning.
2 lessons10 min🔒 Locked
0%
▾
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How Neural Networks Really Work
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