Artificial Intelligence with Python: Your complete guide to building intelligent apps using Python 3.x and TensorFlow 2,2nd Edition
Print Length 页数: 618 pages
Publisher finelybook 出版社: Packt Publishing (January 31,2020)
Language 语言: English
ISBN-10: 183921953X
ISBN-13: 9781839219535
by: Alberto Artasanchez
Book Description
By finelybook
New edition of the bestselling guide to artificial intelligence with Python,updated to Python 3.x and TensorFlow 2,with seven new chapters that cover RNNs,AI & Big Data,fundamental use cases,chatbots,and more.
Artificial Intelligence with Python,Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x and TensorFlow 2. Not only does it provide you an introduction to artificial intelligence,this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications.
This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence,including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data.
Finally,this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems,starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end,you will have gained a solid understanding of,and when best to use,these many artificial intelligence techniques.
What you will learn
Understand what artificial intelligence,machine learning,and data science are
Explore the most common artificial intelligence use cases
Learn how to build a machine learning pipeline
Assimilate the basics of feature selection and feature engineering
Identify the differences between supervised and unsupervised learning
Discover the most recent advances and tools offered for AI development in the cloud
Develop automatic speech recognition systems and chatbots
Understand RNNs and various DL models
Contents
Preface
Chapter 1: Introduction to Artificial Intelligence
Chapter 2: Fundamental Use Cases for Artificial Intelligence
Chapter 3: Machine Learning Pipelines
Chapter 4: Feature Selection and Feature Engineering
Chapter 5: Classification and Regression Using Supervised Learning
Chapter 6: Predictive Analytics with Ensemble Learning
Chapter 7: Detecting Patterns with Unsupervised Learning
Chapter 8: Building Recommender Systems
Chapter 9: Logic Programming
Chapter 10: Heuristic Search Techniques
Chapter 11: Genetic Algorithms and Genetic Programming
Chapter 12: Artificial Intelligence on the Cloud
Chapter 13: Building Games with Artificial Intelligence
Chapter 14: Building a Speech Recognizer
Chapter 15: Natural Language Processing
Chapter 16: Chatbots
Chapter 17: Sequential Data and Time Series Analysis
Chapter 18: Image Recognition
Chapter 19: Neural Networks
Chapter 20: Deep Learning with Convolutional Neural Networks
Chapter 21: Recurrent Neural Networks and Other Deep Learning Models
Chapter 22: Creating Intelligent Agents with Reinforcement Learning
Chapter 23: Artificial Intelligence and Big Data
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