book

Evolutionary Deep Learning

Name: Evolutionary Deep Learning
Author: Micheal Lanham
ISBN: 9781617299520

by Micheal Lanham

August 2023

Intermediate to advanced

360 pages

10h 23m

English

Manning Publications

Audiobook available

Read now

Unlock full access

inside front cover
Evolutionary Deep Learning
Copyright
dedication
contents
front matter
prefaceacknowledgmentsabout this bookWho should read this bookHow this book is organized: A road mapAbout the codeliveBook discussion forumabout the authorabout the cover illustration
Part 1. Getting started
1 Introducing evolutionary deep learning
1.1 What is evolutionary deep learning?1.1.1 Introducing evolutionary computation1.2 The why and where of evolutionary deep learning1.3 The need for deep learning optimization1.3.1 Optimizing the network architecture1.4 Automating optimization with automated machine learning1.4.1 What is automated machine learning?1.5 Applications of evolutionary deep learning1.5.1 Model selection: Weight search1.5.2 Model architecture: Architecture optimization1.5.3 Hyperparameter tuning/optimization1.5.4 Validation and loss function optimization1.5.5 Neuroevolution of augmenting topologies1.5.6 GoalsSummary
2 Introducing evolutionary computation
2.1 Conway’s Game of Life on Google Colaboratory2.2 Simulating life with Python2.2.1 Learning exercises2.3 Life simulation as optimization2.3.1 Learning exercises2.4 Adding evolution to the life simulation2.4.1 Simulating evolution2.4.2 Learning exercises2.4.3 Some background on Darwin and evolution2.4.4 Natural selection and survival of the fittest2.5 Genetic algorithms in Python2.5.1 Understanding genetics and meiosis2.5.2 Coding genetic algorithms2.5.3 Constructing the population2.5.4 Evaluating fitness2.5.5 Selecting for reproduction (crossover)2.5.6 Applying crossover: Reproduction2.5.7 Applying mutation and variation2.5.8 Putting it all together2.5.9 Understanding genetic algorithm hyperparameters2.5.10 Learning exercisesSummary
3 Introducing genetic algorithms with DEAP
3.1 Genetic algorithms in DEAP3.1.1 One max with DEAP3.1.2 Learning exercises3.2 Solving the Queen’s Gambit3.2.1 Learning exercises3.3 Helping a traveling salesman3.3.1 Building the TSP solver3.3.2 Learning exercises3.4 Selecting genetic operators for improved evolution3.4.1 Learning exercises3.5 Painting with the EvoLisa3.5.1 Learning exercisesSummary

4 More evolutionary computation with DEAP
4.1 Genetic programming with DEAP4.1.1 Solving regression with genetic programming4.1.2 Learning exercises4.2 Particle swarm optimization with DEAP4.2.1 Solving equations with PSO4.2.2 Learning exercises4.3 Coevolving solutions with DEAP4.3.1 Coevolving genetic programming with genetic algorithms4.4 Evolutionary strategies with DEAP4.4.1 Applying evolutionary strategies to function approximation4.4.2 Revisiting the EvoLisa4.4.3 Learning exercises4.5 Differential evolution with DEAP4.5.1 Approximating complex and discontinuous functions with DE4.5.2 Learning exercisesSummary
Part 2. Optimizing deep learning
5 Automating hyperparameter optimization
5.1 Option selection and hyperparameter tuning5.1.1 Tuning hyperparameter strategies5.1.2 Selecting model options5.2 Automating HPO with random search5.2.1 Applying random search to HPO5.3 Grid search and HPO5.3.1 Using grid search for automatic HPO5.4 Evolutionary computation for HPO5.4.1 Particle swarm optimization for HPO5.4.2 Adding EC and DEAP to automatic HPO5.5 Genetic algorithms and evolutionary strategies for HPO5.5.1 Applying evolutionary strategies to HPO5.5.2 Expanding dimensions with principal component analysis5.6 Differential evolution for HPO5.6.1 Differential search for evolving HPOSummary
6 Neuroevolution optimization
6.1 Multilayered perceptron in NumPy6.1.1 Learning exercises6.2 Genetic algorithms as deep learning optimizers6.2.1 Learning exercises6.3 Other evolutionary methods for neurooptimization6.3.1 Learning exercises6.4 Applying neuroevolution optimization to Keras6.4.1 Learning exercises6.5 Understanding the limits of evolutionary optimization6.5.1 Learning exercisesSummary
7 Evolutionary convolutional neural networks
7.1 Reviewing convolutional neural networks in Keras7.1.1 Understanding CNN layer problems7.1.2 Learning exercises7.2 Encoding a network architecture in genes7.2.1 Learning exercises7.3 Creating the mating crossover operation7.4 Developing a custom mutation operator7.5 Evolving convolutional network architecture7.5.1 Learning exercisesSummary
Part 3. Advanced applications
8 Evolving autoencoders
8.1 The convolution autoencoder8.1.1 Introducing autoencoders8.1.2 Building a convolutional autoencoder8.1.3 Learning exercises8.1.4 Generalizing a convolutional AE8.1.5 Improving the autoencoder8.2 Evolutionary AE optimization8.2.1 Building the AE gene sequence8.2.2 Learning exercises8.3 Mating and mutating the autoencoder gene sequence8.4 Evolving an autoencoder8.4.1 Learning exercises8.5 Building variational autoencoders8.5.1 Variational autoencoders: A review8.5.2 Implementing a VAE8.5.3 Learning exercisesSummary
9 Generative deep learning and evolution
9.1 Generative adversarial networks9.1.1 Introducing GANs9.1.2 Building a convolutional generative adversarial network in Keras9.1.3 Learning exercises9.2 The challenges of training a GAN9.2.1 The GAN optimization problem9.2.2 Observing vanishing gradients9.2.3 Observing mode collapse in GANs9.2.4 Observing convergence failures in GANs9.2.5 Learning exercises9.3 Fixing GAN problems with Wasserstein loss9.3.1 Understanding Wasserstein loss9.3.2 Improving the DCGAN with Wasserstein loss9.4 Encoding the Wasserstein DCGAN for evolution9.4.1 Learning exercises9.5 Optimizing the DCGAN with genetic algorithms9.5.1 Learning exercisesSummary
10 NEAT: NeuroEvolution of Augmenting Topologies
10.1 Exploring NEAT with NEAT-Python10.1.1 Learning exercises10.2 Visualizing an evolved NEAT network10.3 Exercising the capabilities of NEAT10.3.1 Learning exercises10.4 Exercising NEAT to classify images10.4.1 Learning exercises10.5 Uncovering the role of speciation in evolving topologies10.5.1 Tuning NEAT speciation10.5.2 Learning exercisesSummary
11 Evolutionary learning with NEAT
11.1 Introducing reinforcement learning11.1.1 Q-learning agent on the frozen lake11.1.2 Learning exercises11.2 Exploring complex problems from the OpenAI Gym11.2.1 Learning exercises11.3 Solving reinforcement learning problems with NEAT11.3.1 Learning exercises11.4 Solving Gym’s lunar lander problem with NEAT agents11.4.1 Learning exercises11.5 Solving Gym’s lunar lander problem with a deep Q-networkSummary
12 Evolutionary machine learning and beyond
12.1 Evolution and machine learning with gene expression programming12.1.1 Learning exercises12.2 Revisiting reinforcement learning with Geppy12.2.1 Learning exercises12.3 Introducing instinctual learning12.3.1 The basics of instinctual learning12.3.2 Developing generalized instincts12.3.3 Evolving generalized solutions without instincts12.3.4 Learning exercises12.4 Generalized learning with genetic programming12.4.1 Learning exercises12.5 The future of evolutionary machine learning12.5.1 Is evolution broken?12.5.2 Evolutionary plasticity12.5.3 Improving evolution with plasticity12.5.4 Computation and evolutionary search12.6 Generalization with instinctual deep and deep reinforcement learningSummary
Appendix.
A.1 Accessing the source codeA.2 Running code on other platforms
index
inside back cover

Content preview from Evolutionary Deep Learning

Appendix.

A.1 Accessing the source code

The source code for the book is located at https://github.com/cxbxmxcx/EvolutionaryDeepLearning. You are not required to download any code to your local machine for this code; all the code will run on Google Colaboratory, or Colab for short. To open your first notebook, follow these steps:

Navigate to https://github.com/cxbxmxcx/EvolutionaryDeepLearning, and open one of the sample notebooks by clicking the link shown in figure A.1.

Figure A.1 Clicking a notebook link in the repository code view
This will open a view of the notebook. At the top will be a Colab badge (figure A.2). Click on the badge ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

The Human Factor in AI-Based Decision-Making

Publisher Resources

ISBN: 9781617299520Publisher Support Publisher Website

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

Evolutionary Deep Learning

by Micheal Lanham

Appendix.

A.1 Accessing the source code

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.