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
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 forThis 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.
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