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

Name: Artificial Intelligence with Python Cookbook: Proven recipes for applying AI algorithms and deep learning techniques using TensorFlow 2.x and PyTorch 1.6
Rating: 4.25 (4 reviews)

Ritesh Kumar · Ben Auffarth

2020年10月 · Packt Publishing Ltd

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电子书

468

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关于此电子书

Work through practical recipes to learn how to solve complex machine learning and deep learning problems using PythonKey Features

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

Book DescriptionArtificial 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.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

Who this book is 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.

Work through practical recipes to learn how to solve complex machine learning and deep learning problems using PythonKey Features 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 ...

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作者简介

Ritesh Kumar is an experienced researcher with a demonstrated history of working on various industry-sponsored projects mainly on Data Analysis, Machine Learning, and Intelligent Instrumentation. Currently Ritesh is associated with the University of Hertfordshire.

Ben Auffarth is a full-stack data scientist with more than 15 years of work experience. With a background and Ph.D. in computational and cognitive neuroscience, he has designed and conducted wet lab experiments on cell cultures, analyzed experiments with terabytes of data, run brain models on IBM supercomputers with up to 64k cores, built production systems processing hundreds and thousands of transactions per day, and trained language models on a large corpus of text documents. He co-founded and is the former president of Data Science Speakers, London.