AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence

  • 9h 17m 55s
  • Laurence Moroney
  • Gildan Media
  • 2023

If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics.

You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code.

You'll learn: how to build models with TensorFlow using skills that employers desire; the basics of machine learning by working with code samples; how to implement computer vision, including feature detection in images; how to use NLP to tokenize and sequence words and sentences; methods for embedding models in Android and iOS; and how to serve models over the web and in the cloud with TensorFlow Serving.

About the Author

Laurence Moroney leads AI Advocacy at Google. His goal is to educate the world of software developers in how to build AI systems with Machine Learning. He's a frequent contributor to the TensorFlow YouTube channel at youtube.com/tensorflow, a recognized global keynote speaker and author of more books than he can count, including several best-selling science fiction novels, and a produced screenplay. He's based in Sammamish, Washington where he drinks way too much coffee.

In this Audiobook

  • Chapter 1 - Introduction to TensorFlow
  • Chapter 2 - Introduction to Computer Vision
  • Chapter 3 - Going Beyond the Basics: Detecting Features in Images
  • Chapter 4 - Using Public Datasets with TensorFlow Datasets
  • Chapter 5 - Introduction to Natural Language Processing
  • Chapter 6 - Making Sentiment Programmable Using Embeddings
  • Chapter 7 - Recurrent Neural Networks for Natural Language Processing
  • Chapter 8 - Using TensorFlow to Create Text
  • Chapter 9 - Understanding Sequence and Time Series Data
  • Chapter 10 - Creating ML Models to Predict Sequences
  • Chapter 11 - Using Convolutional and Recurrent Methods for Sequence Models
  • Chapter 12 - An Introduction to TensorFlow Lite
  • Chapter 13 - Using TensorFlow Lite in Android Apps
  • Chapter 14 - Using TensorFlow Lite in iOS Apps
  • Chapter 15 - An Introduction to TensorFlow.js
  • Chapter 16 - Coding Techniques for Computer Vision in TensorFlow.js
  • Chapter 17 - Reusing and Converting Python Models to JavaScript
  • Chapter 18 - Transfer Learning in JavaScript
  • Chapter 19 - Deployment with TensorFlow Serving
  • Chapter 20 - AI Ethics, Fairness, and Privacy
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