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Learn Hopfield networks and neural networks (and back-propagation) theory and implementation in Python
What you'll learn:- Hopfield neural networks theory
- Hopfield neural network implementation in Python
- Neural neural networks theory
- Neural networks implementation
- Loss functions
- Gradient descent and back-propagation algorithms
This course is about artificial neural networks. Artificial intelligence and machine learning are getting more and more popular nowadays. In the beginning, other techniques such as Support Vector Machines outperformed neural networks, but in the 21st century neural networks again gain popularity. In spite of the slow training procedure, neural networks can be very powerful. Applications ranges from regression problems to optical character recognition and face detection.
Section 1:
what are Hopfield neural networks
modeling the human brain
the big picture behind Hopfield neural networks
Section 2:
Hopfield neural networks implementation
auto-associative memory with Hopfield neural networks
Section 3:
what are feed-forward neural networks
modeling the human brain
the big picture behind neural networks
Section 4:
feed-forward neural networks implementation
gradient descent with back-propagation
In the first part of the course you will learn about the theoretical background of neural networks, later you will learn how to implement them in Python from scratch.
If you are keen on learning machine learning methods, let's get started!
Overview
