Hands-On Artificial Intelligence for Beginners

Develop real-world AI apps using this practical and hands-on guide
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Hands-On Artificial Intelligence for Beginners

Patrick Smith

Develop real-world AI apps using this practical and hands-on guide
This title is available to pre-order now and is expected to be published in
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Book Details

ISBN 139781788991063
Paperback474 pages

Book Description

Virtual Assistants like Alexa and Siri are understanding most of our requests, Facebook is recognizing our faces, Google's cars are reading addresses off of buildings, Amazon's prices are decided by AI's, Netflix uses AI to compress its video and much more. With this book you will be able to understand the science behind designing and developing intelligent applications and teach you to build one.

This book teaches to harness packages like TensorFlow, Keras, Pytorch and GPUs. We will review recent changes in AI and learn how simple neural nets can simulate standard statistical techniques. These include regression, support vector machines, linear discriminant analysis, and mixture of Gaussians. You’ll come across different types of neural nets like convolutional, recurrent, recursive and other forms of neural nets which can identify patterns in images, sound and process textual information. Later part will be about applying transfer learning and adversarial neural networks to design smart games, recognize and regenerate images, automatic machine translation and much more . We will also apply deep learning to plan limb motion and aid in path planning for robots.

Towards the end of this book, you will understand the considerations for optimizing methods for artificial neural nets and how to deploy and maintain AI applications.

Table of Contents

What You Will Learn

  • Design basic deep learning models for classification and regression
  • Build Classifiers with logistic regression, support vector machines
  • Create a mixture of Gaussians and neural nets for machine translation
  • Build your first Deep Neural Net for photo analysis and classification
  • Apply Convolution to identify patterns in images and speech data
  • Implement Upper Confidence Trees for a Tic-Tac-Toe
  • Apply Deep Learning to train the neural net with self play
  • Solve optimization problem, gradient decent, Gibbs sampling, and BFGS

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Table of Contents

Book Details

ISBN 139781788991063
Paperback474 pages
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