finelybook
Hands-On Artificial Intelligence for Beginners: An introduction to AI concepts,algorithms,and their implementation
Authors: Patrick D. Smith
ISBN-10 书号： 1788991060
ISBN-13 书号： 9781788991063
Release Finelybook 出版日期： 2018-10-31
pages 页数： 362 pages
Publisher Finelybook 出版社： Packt
Book Description
Grasp the fundamentals of Artificial Intelligence and build your own intelligent systems with ease
Virtual Assistants,such as Alexa and Siri,process our requests,Google’s cars have started to read addresses,and Amazon’s prices and Netflix’s recommended videos are decided by AI. Artificial Intelligence is one of the most exciting technologies and is becoming increasingly significant in the modern world.
Hands-On Artificial Intelligence for Beginners will teach you what Artificial Intelligence is and how to design and build intelligent applications. This book will teach you to harness packages such as TensorFlow in order to create powerful AI systems. You will begin with reviewing the recent changes in AI and learning how artificial neural networks (ANNs) have enabled more intelligent AI. You’ll explore feedforward,recurrent,convolutional,and generative neural networks (FFNNs,RNNs,CNNs,and GNNs),as well as reinforcement learning methods. In the concluding chapters,you’ll learn how to implement these methods for a variety of tasks,such as generating text for chatbots,and playing board and video games.
By the end of this book,you will be able to understand exactly what you need to consider when optimizing ANNs and how to deploy and maintain AI applications.
What you will learn
Use TensorFlow packages to create AI systems
Build feedforward,convolutional,and recurrent neural networks
Implement generative models for text generation
Build reinforcement learning algorithms to play games
Assemble RNNs,CNNs,and decoders to create an intelligent assistant
Utilize RNNs to predict stock market behavior
Create and scale training pipelines and deployment architectures for AI systems
contents
1 The History of AI
2 Machine Learning Basics
3 Platforms and Other Essentials
4 Your First Artificial Neural Networks
5 Convolutional Neural Networks
6 Recurrent Neural Networks
7 Generative Models
8 Reinforcement Learning
9 Deep Learning for Intelligent Agents
10 Deep Learning for Game Playing
11 Deep Learning for Finance
12 Deep Learning for Robotics
13 Deploying and Maintaining AI Applications
