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

Requirements

Very basic Python

Description

Artificial intelligence and machine learning are getting more and more popular nowadays. In the bning, 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!

Who this course is for:

Bner Python developers curious about data science



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