Search Torrents
|
Browse Torrents
|
48 Hour Uploads
|
TV shows
|
Music
|
Top 100
Audio
Video
Applications
Games
Porn
Other
All
Music
Audio books
Sound clips
FLAC
Other
Movies
Movies DVDR
Music videos
Movie clips
TV shows
Handheld
HD - Movies
HD - TV shows
3D
Other
Windows
Mac
UNIX
Handheld
IOS (iPad/iPhone)
Android
Other OS
PC
Mac
PSx
XBOX360
Wii
Handheld
IOS (iPad/iPhone)
Android
Other
Movies
Movies DVDR
Pictures
Games
HD - Movies
Movie clips
Other
E-books
Comics
Pictures
Covers
Physibles
Other
Details for:
Sheppard Clinton. Genetic Algorithms with Python 2019
sheppard clinton genetic algorithms python 2019
Type:
E-books
Files:
1
Size:
8.5 MB
Uploaded On:
July 9, 2020, 10:51 a.m.
Added By:
andryold1
Seeders:
1
Leechers:
0
Info Hash:
B5113F091B7D796BFB5EFE5A9E151CB65558F849
Get This Torrent
Textbook in PDF format Get a hands-on introduction to machine learning with genetic algorithms using Python. Genetic algorithms are one of the tools you can use to apply machine learning to finding good, sometimes even optimal, solutions to problems that have billions of potential solutions. This book gives you experience making genetic algorithms work for you, using easy-to-follow example problems that you can fall back upon when learning to use other machine learning tools and techniques. Each chapter begins with a project which you are encouraged to try to implement on your own before working through one possible implementation, and related pitfalls, with the author. This helps to build your skills at using genetic algorithms and prepares you to solve problems in your own field of expertise. The projects start with Hello World! then progress toward optimizing one genetic algorithm with another, and finally genetic programming. The following topics are introduced just-in-time: different ways to determine fitness, handling competing goals, phenotypes and genotypes, mutation options, memetic algorithms, local minimums and maximums, simulated annealing, branch and bound, variable length chromosomes, crossover, tuning genetic algorithms, symbolic genetic programming, automatically defined functions, hill climbing, chromosome repair, and tournament selection. Python is used as the teaching language in this book because it is a high-level, low ceremony, and powerful language whose code can be easily understood even by entry-level programmers. Because Python is used for teaching, but is not being taught in this book, the use of Python-specific features that might make the code harder to follow for non-Python programmers has been minimized. This means that if you have experience with another programming language then you should have no difficulty using this book to learn about genetic algorithms while learning to at least read Python. Additionally, it should not be difficult for you to translate the working code used in this book to your favorite programming language on-the-fly, depending on the capabilities and support libraries available for your preferred language. For a brief introduction to genetic algorithms and the writing style used in this book, use Amazon’s Look Inside feature, or use your Kindle Unlimited subscription to try it out, or download the sample chapters linked from the Github repository associated with this book. The source code is made available under the Apache License, Version 2.0
Get This Torrent
Sheppard Clinton. Genetic Algorithms with Python 2019.pdf
8.5 MB
