Evolving Architectures NEAT - Learn Data Science with Travis

Evolving-Architectures-NEAT

Evolving architectures, particularly through the NeuroEvolution of Augmenting Topologies (NEAT) algorithm, represent a significant advancement in the field of artificial intelligence and machine learning. NEAT combines genetic algorithms with neural networks to evolve complex architectures over time, allowing for the automatic design of neural networks that can adapt to various tasks. This approach not only optimizes the structure of the networks but also enhances their performance by evolving both the weights and the topology. As a result, NEAT has become a powerful tool for tackling challenging problems in AI, enabling the development of innovative solutions that traditional methods may struggle to achieve.

HyperNEAT: Powerful, Indirect Neural Network Evolution

Towards Data Science

Last week, I wrote an article about NEAT (NeuroEvolution of Augmenting Topologies) and we discussed a lot of the cool things that surrounded the algorithm. We also briefly touched upon how this older…...

The Evolved Transformer — Enhancing Transformer with Neural Architecture Search

Towards Data Science

Neural architecture search (NAS) is the process of algorithmically searching for new designs of neural networks. Though researchers have developed sophisticated architectures over the years, the…

Evolutionary Architecture: Supporting Constant Change

Better Programming

Through continuous improvement, technology adoption, and not being complacent The first principle of an evolutionary architecture is to enable incremental change in an architecture over time — Though...

Architectures — Part 1

Towards Data Science

In this lecture, we look into the early deep architectures starting from LeNet over AlexNet all the way to GoogLeNet.

Evolving Deep Neural Networks

Towards Data Science

Deep learning architectures are getting harder to design, but evolutionary algorithms may help us overcome this. This review presents important recent research in this matter.

How do we teach a machine to program itself ? — NEAT learning.

Towards Data Science

In this post, I will try to explain a method of machine learning called Evolving Neural Networks through Augmenting Topologies (NEAT). I love to learn. It is really exciting to open up a book or a…

The Architecture and Implementation of LeNet-5

Towards AI

LeNet-5- The very oldest Neural Network Architecture. This network was trained on MNIST data and it is a 7 layered architecture given by Yann Lecun.

Architectures — Part 2

Towards Data Science

In this lecture we explore deeper architectures auch as Inception V2 and V3 and explain the concept of exponential feature reuse.

Architectures — Part 4

Towards Data Science

In this lecture, we look at different ideas how to develop ResNets further. In particular, we look into ideas on how to combine this with other architectures.

Neuroevolution — evolving Artificial Neural Networks topology from the scratch

Becoming Human: Artificial Intelligence Magazine

This article presents how to build and train Artificial Neural Networks by NEAT algorithm. It will consider weakness of current Gradient Descent based training methods and shows a way to improve it.

ENet — A Deep Neural Architecture for Real-Time Semantic Segmentation

Towards Data Science

This is a paper summary of the paper: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation by Adam Paszke Paper: https://arxiv.org/abs/1606.02147 ENet (Efficient Neural…