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Machine Learning™ - Neural Networks from Scratch [Python]

Learn Hopfield networks and neural networks (and back-propagation) theory and implementation in Python

4.50 (30 reviews)

Students

3.5 hours

Content

Nov 2020

Last Update
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What you will 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


Description

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!


Screenshots

Machine Learning™ - Neural Networks from Scratch [Python]
Machine Learning™ - Neural Networks from Scratch [Python]
Machine Learning™ - Neural Networks from Scratch [Python]
Machine Learning™ - Neural Networks from Scratch [Python]

Content

Introduction

Introduction

Artificial Intelligence Basics

Why to learn artificial intelligence and machine learning?

Types of artificial intelligence learning methods

Neural Networks With Backpropagation Theory

Artificial neural networks - inspiration

Artificial neural networks - layers

Artificial neural networks - the model

Why to use activation functions?

Neural networks - the big picture

Using bias nodes in the neural network

How to measure the error of the network?

Optimization with gradient descent

Gradient descent with backpropagation

Single Perceptron Model

Perceptron model training

Perceptron model implementation I

Perceptron model implementation II

Trying to solve XOR problem

Conclusion: linearity and hidden layers

Backpropagation Implementation

Backpropagation implementation I

Backpropagation implementation II

Backpropagation implementation III

Backpropagation implementation IV

Backpropagation implementation V

Testing the Neural Network

Testing the network

Next Steps

Next steps in machine learning

Course Materials (DOWNLOADS)

Course materials


Reviews

A
Arthur26 December 2020

The lecturer is great but I'm the problem. I have no background in this and it's hard to assimilate the info. That being said, let's continue.


Coupons

DateDiscountStatus
5/15/202150% OFFExpired

3514608

Udemy ID

9/20/2020

Course created date

9/23/2020

Course Indexed date
Angelcrc Seven
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