Artificial Intelligence with Python Cookbook

Work through practical recipes to learn how to solve complex machine learning and deep learning problems using Python Key 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 im...

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Detalles Bibliográficos
Autor principal: Auffarth, Ben
Formato: Electrónico eBook
Idioma:Indeterminado
Publicado: [S.l.] : Packt Publishing, 2020.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

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520 |a Work through practical recipes to learn how to solve complex machine learning and deep learning problems using Python Key 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 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. 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 in... 
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