Neuroevolution- Genetic Algorithms And Artificial NeuralNets

Neuroevolution: Genetic Algorithms And Artificial NeuralNets, available at Free, has an average rating of 4.4, with 9 lectures, based on 47 reviews, and has 3662 subscribers.

You will learn about How Evolutionary algorithms works Artificial Neural Networks How to train a model to play different games Alternative way to train Artificial Neural networks This course is ideal for individuals who are Web developers curious about deep learning and AI It is particularly useful for Web developers curious about deep learning and AI.

Enroll now: Neuroevolution: Genetic Algorithms And Artificial NeuralNets

Summary

Title: Neuroevolution: Genetic Algorithms And Artificial NeuralNets

Price: Free

Average Rating: 4.4

Number of Lectures: 9

Number of Published Lectures: 8

Number of Curriculum Items: 9

Number of Published Curriculum Objects: 8

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

How Evolutionary algorithms works

Artificial Neural Networks

How to train a model to play different games

Alternative way to train Artificial Neural networks

Who Should Attend

Web developers curious about deep learning and AI

Target Audiences

Web developers curious about deep learning and AI

Neuroevolution is a powerful approach to machine learning and artificial intelligence that uses evolutionary algorithms to evolve neural networks.

Most neural networks use gradient descent rather than neuroevolution. However, around 2017 researchers at Uber stated they had found that simple structural neuroevolution algorithms were competitive with sophisticated modern industry-standard gradient-descent deep learning algorithms.

Deep Neuroevolution: Genetic Algorithms are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

This course introduces students to the principles of neuroevolution and the techniques used to design and implement neuroevolution algorithms.

The course covers the following topics:

1. Introduction to neuroevolution: basic principles and applications

2. Evolutionary algorithms: genetic algorithms, genetic programming, and evolutionary strategies

3. Neural networks: types, architectures, and training techniques

4. Neuroevolution algorithms: evolutionary algorithms applied to neural networks

5. Applications of neuroevolution: games, and optimization problems

6. Advanced topics: multi-objective neuroevolution, neuroevolution of recurrent neural networks, and deep neuroevolution.

In this project, we have applied GeneticEvolution to multiple games such as self-driving cars, smart caps and flappy bird.

This course is a follow-up to my other course about Artificial Neural Networks from scratch, where I show how to create an ANN from scratch without libraries. In that project, the learning process is done using backpropagation(gradient descent), this project uses a different approach. We will use Evolutionary Algorithm.

By following this course until the end,  students will have a solid understanding of the principles of neuroevolution and the ability to design and implement neuroevolution algorithms for a variety of applications.

Course Curriculum

Chapter 1: Neuroevolution

Lecture 1: Introduction

Lecture 2: Source code

Lecture 3: Feedback and support

Lecture 4: What is Neuroevolution?

Lecture 5: Artificial Neural Networks

Lecture 6: Neuralevolution training

Lecture 7: NeuroEvolution library

Lecture 8: Visualise the network behaviour during the training

Instructors

Alexsandro Souza
Tech Lead

Rating Distribution

1 stars: 1 votes

2 stars: 0 votes

3 stars: 10 votes

4 stars: 12 votes

5 stars: 24 votes

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