Neuroevolution: Genetic Algorithms And Artificial NeuralNets | free course Data Science Artificial Intelligence


Neuroevolution: Genetic Algorithms And Artificial NeuralNets | Data Science Artificial Intelligence free course 

How to combine Artificial Neural network and Genetics Algorithms to build powerful AI

Neuroevolution: Genetic Algorithms And Artificial NeuralNets | Data Science Artificial Intelligence free course 

Neuroevolution is a powerful approach to machine learning and artificial intelligence that uses evolutionary algorithms to develop neural networks.
Most neural networks use gradient descent instead of neuroevolution. Around 2017, Uber researchers announced that they had found simple structural neuroevolutionary algorithms competitive with advanced modern industry standard gradient descent algorithms.
Deep neurodevelopment: genetic algorithms as a competitive alternative to training deep neural networks for reinforcement learning
This course introduces students to neuroevolutionary principles and techniques used in the design and implementation of neuroevolutionary algorithms.
The course covers the following topics:
Introduction to Neuroevolution: Basic Principles and Applications
Evolutionary Algorithms: Genetic Algorithms, Genetic Programming and Evolutionary Strategies
Neural Networks: Types, Architectures and Training Techniques
Neuroevolutionary Algorithms: Evolutionary Algorithms Applied to Neural Networks
Applications of Neuroevolution: Games and Optimization Problems
Advanced Topics: Multi-Objective Neuroevolution, Recurrent Neural Networks Neuroevolution, and Deep Neuroevolution. In this project, we applied GeneticEvolution to several games such as self-driving cars, smart tips and Flappy bird.
This course is a continuation of my second course on Artificial Neural Networks from scratch, where I show how to build an ANN from scratch without any libraries. In this project, the learning process is done using back propagation (gradient descent), a different approach is used in this project. We use an evolutionary algorithm.
Upon completion of this course, students will have a good understanding of neuroevolutionary principles and the ability to design and implement neuroevolutionary algorithms for a variety of applications. Who this course is for:
Web developers are interested in deep learning and artificial intelligence

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