Practical AI on the Google Cloud Platform: Learn How to Use the Latest AI Cloud Services on the Google Cloud Platform

✍ Scribed by Micheal Lanham

• Publisher: O'Reilly Media, Inc.
• Year: 2020
• Tongue: English
• Category: Library

✦ Synopsis

Book Description
AI is complicated, but cloud providers have stepped in to make it easier, offering free (or affordable) state-of-the-art models and training tools to get you started. In this book, AI novices will learn how to use Google’s AI-powered cloud services to do everything from analyzing text, images, and video to creating a chatbot.

Author Micheal Lanham takes you step-by-step through building models, training them, and then expanding on them to accomplish increasingly complex tasks. If you have a good grasp of math and the Python language, this book will get you up and running with Google Cloud Platform, whether you’re looking to build a simple business AI application or an AI assistant.

Learn key concepts for data science, machine learning, and deep learning
Explore tools like Video AI, AutoML Tables, the Cloud Inference API, the Recommendations AI API, and BigQuery ML
Perform image recognition using CNNs, transfer learning, and GANs
Build a simple language processor using embeddings, RNNs, and Bidirectional Encoder
Representations from Transformers (BERT)
Use Dialogflow to build a chatbot
Analyze video with automatic video indexing, face detection, and TF Hub

✦ Table of Contents

Table of Contents
Preface
Who Should Read this Book
Why I Wrote this Book
Navigating this Book
A Note on the Google AI Platform
Things You Need for this Book
Conventrions Used in this Book
Using Code Examples
O’Reilly Online Learning
How to Contact Us
Acknowledgments
1. Data Science and Deep Learning
What is Data Science?
Classification and Regression
Regression
Goodness of Fit
Classification with Logistic Regression
Multi-variant Regression and Classification
Data Discovery and Preparation
Preparing Data
Bad Data
Training, Test and Validation Data
Good Data
Preparing Data
Questioning Your Data
The Basics of Deep Learning
The Perceptron Game
Understanding How Networks Learn
Backpropagation
Optimization and Gradient Descent
Vanishing or Exploding Gradients
SGD and Batching Samples
Batch Normalization and Regularization
Activation Functions
Loss Functions
Building a Deep Learner
Overfitting and Underfitting
Network Capacity
Conclusion
2. AI on the Google Cloud Platform
AI Services on GCP
Google Colab Notebooks
AutoML Tables
The Cloud Shell
Managing Cloud Data
Conclusion