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Artificial Intelligence with Python Cookbook
Artificial Intelligence with Python Cookbook

Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6

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Profile Icon Kumar Profile Icon Ben Auffarth
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$12.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (7 Ratings)
Paperback Oct 2020 468 pages 1st Edition
eBook
$29.99
Paperback
$43.99
Subscription
Free Trial
Renews at $12.99p/m
Arrow left icon
Profile Icon Kumar Profile Icon Ben Auffarth
Arrow right icon
$12.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (7 Ratings)
Paperback Oct 2020 468 pages 1st Edition
eBook
$29.99
Paperback
$43.99
Subscription
Free Trial
Renews at $12.99p/m
eBook
$29.99
Paperback
$43.99
Subscription
Free Trial
Renews at $12.99p/m

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Key benefits

  • Get up and running with artificial intelligence in no time using hands-on problem-solving recipes
  • Explore popular Python libraries and tools to build AI solutions for images, text, sounds, and images
  • Implement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much more

Description

Artificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research. Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you’ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you’ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems. By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production.

Who is this book for?

This AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. You’ll also find this book useful if you’re looking for state-of-the-art solutions to perform different machine learning tasks in various use cases. Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively in this book.

What you will learn

  • Implement data preprocessing steps and optimize model hyperparameters
  • Delve into representational learning with adversarial autoencoders
  • Use active learning, recommenders, knowledge embedding, and SAT solvers
  • Get to grips with probabilistic modeling with TensorFlow probability
  • Run object detection, text-to-speech conversion, and text and music generation
  • Apply swarm algorithms, multi-agent systems, and graph networks
  • Go from proof of concept to production by deploying models as microservices
  • Understand how to use modern AI in practice

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Oct 30, 2020
Length: 468 pages
Edition : 1st
Language : English
ISBN-13 : 9781789133967
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Product Details

Publication date : Oct 30, 2020
Length: 468 pages
Edition : 1st
Language : English
ISBN-13 : 9781789133967
Category :
Languages :
Tools :

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Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.9
(7 Ratings)
5 star 85.7%
4 star 14.3%
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Andreas Mueller Dec 09, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I've been impressed by the wide overview of the book, which really spans the gamut of what AI means, from classification to search algorithms and A/B testing. The book focuses on some standard tools but also branches out to surface some lesser known libraries that can come in handy.While 468 pages can only give a taste of each topic, the book is jam-packed with examples and serves as a good starting point with plenty of references.
Amazon Verified review Amazon
Murphy Choy Aug 26, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book is a great recipe book for folks who want quick results without going through reams of content. I love the easy to read structure of this book.
Amazon Verified review Amazon
Amazon Customer Mar 21, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a great book, I believe there is something new and useful to you no matter how good you are at maching learning.For beginners, the topics for each part are very clear and well organized. The book will introduce the type of the problem it's going to talk about, the models useful to solve these problems, math and statistics behind the methods. All of these are presented with examples and step by step analysis and visualization, give you a step-by-step instruction for a maching learning project.For ML practitioners who want to learn new topics, like entering NLP, CV, RL or time series, you can find the chapters useful for you easily in the table of content. Just follow the codes and learn the concepts and you are good to read the papers with a good knowledge of how to write general codes in the area.For researchers, you can find some good engineering tricks, from using jupyter notebook more efficiently to some good modules to improve your code efficiency.And for those who have already learned how to do all above, this book has a good writing style with excellent structure for you to learn~
Amazon Verified review Amazon
Isaac Dec 03, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
There are a number of survey books on the market for doing AI with python. I own many of them. Auffarth's approach here is unique in that it takes a do then explain the approach. The chapters are well structured with cookbook examples followed by in-depth explanations. What the author calls 'how to do it' then 'how it works.' The breath covered in the book is also impressive. everything from setting up a basic environment to deploying models into production. Applications range from typical deep learning stuff (like predicting housing prices) to image processing, audio applications, and fraud detection. This has become my latest 'go-to' book when I want to explore a new AI topic. I would recommend it to any data scientist looking to take their skills to the next level.
Amazon Verified review Amazon
Eric D. Weber Apr 26, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book has the right mixture of breadth and depth, which is extremely challenging in the (growing) group of AI literature. What I enjoy the most is that you can both learn a lot of Python and/or learn a lot about ML and Experimentation, depending on your focus. Maybe most important, it allows those newer or intermediate in the field to scale up quickly. For beginners and practitioners, there is a lot of value to be found in the 400+ pages.
Amazon Verified review Amazon
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