Python: Beginner's Guide to Artificial Intelligence

Develop real-world applications powered by the latest advances in intelligent systems

Python: Beginner's Guide to Artificial Intelligence

Denis Rothman et al.
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Develop real-world applications powered by the latest advances in intelligent systems
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Book Details

ISBN 139781789957327
Paperback676 pages

Book Description

This Learning Path offers practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. You will be introduced to various machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. You'll find a new balance of classical ideas and modern insights into machine learning. You will learn to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open-source Python libraries.

Throughout the Learning Path, you'll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, and Autoencoders. Discover how to attain deep learning programming on GPU in a distributed way.

By the end of this Learning Path, you know the fundamentals of AI and have worked through a number of case studies that will help you apply your skills to real-world projects.

This Learning Path includes content from the following Packt products:

  • Artificial Intelligence By Example by Denis Rothman
  • Python Deep Learning Projects by Matthew Lamons, Rahul Kumar, and Abhishek Nagaraja
  • Hands-On Artificial Intelligence with TensorFlow by Amir Ziai, Ankit Di

Table of Contents

Chapter 8: Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies
Chapter 10: Applying Biomimicking to Artificial Intelligence
Chapter 19: Handwritten Digits Classification Using ConvNets

What You Will Learn

  • Use adaptive thinking to solve real-life AI case studies
  • Rise beyond being a modern-day factory code worker
  • Understand future AI solutions and adapt quickly to them
  • Master deep neural network implementation using TensorFlow 
  • Predict continuous target outcomes using regression analysis
  • Dive deep into textual and social media data using sentiment analysis

Authors

Table of Contents

Chapter 8: Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies
Chapter 10: Applying Biomimicking to Artificial Intelligence
Chapter 19: Handwritten Digits Classification Using ConvNets

Book Details

ISBN 139781789957327
Paperback676 pages
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