| Publisher | Manning (July 18, 2023) |
| Language | English |
| Paperback | 360 pages |
| ISBN-10 | 1617299529 |
| ISBN-13 | 978-1617299520 |
| Item Weight | 1.35 pounds |
| Dimensions | 7.38 x 0.9 x 9.25 inches |
| Best Sellers Rank | #1,515,388 in Books (See Top 100 in Books) #554 in Computer Neural Networks #898 in Data Processing #74,535 in Type |
| Customer Reviews | 4.3 out of 5 stars 4Reviews |
Exciting new ways to improve current applications of machine learning and generative modeling!
In Evolutionary Deep Learning, author and deep learning guru Micheal Lanham explains the basics of EA and DL, how to combine them, and why you should.
In this hands-on guide, explore real applications of regression, classification, generative modeling, and reinforcement learning. Expand your knowledge to new areas in AI and ML, whether you’re an aspiring, novice, or experienced data scientist.
A toolbelt for robust DL systems. In Chapter 1, get an introduction to evolutionary deep learning, a set of tools that help automate the development of effective, robust DL systems.
Take a DEAP dive! In Chapter 3, tackle larger, more complex genetic algorithms using DEAP, an evolutionary computation (EC) toolkit that makes coding easier!
The Importance of good genes… In Chapter 7, go hands-on to encode the network architecture of a CNN model into genes as you delve into this and other essentials for improving your network architecture.
Notorious GANs! In Chapter 9, explore the fundamentals of GANs and discover how to optimize the process of training these notoriously difficult-to-train models.
Take a step back… In Chapter 12, set aside deep learning for the moment, and take a step forward into new opportunities as we apply evolutionary solutions to other forms of machine learning.
Evolutionary Deep Learning introduces the next wave of AI, showcasing ways to improve everything from hyperparameters and network architecture to generative deep learning and deep reinforcement learning.
This hands-on guide covers a wide range of DL methods, guiding you through practical projects that focus on optimizing DL solutions.
Take hold of the power of evolutionary deep learning, and build DL systems that react quickly to changing business requirements in an efficient (ideally automated) way.
