4.50 (30 reviews)

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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

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

Date | Discount | Status | ||
---|---|---|---|---|

5/15/2021 | 50% OFF | Expired | ||

Angelcrc Seven

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