Working with AI is complicated and expensive for many developers. That's why 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. With this book, you'll learn how to use Google's AI-powered cloud services to do everything from creating a chatbot to analyzing text, images, and video.
Author Micheal Lanham demonstrates methods for building and training models step-by-step and shows you how to expand your models to accomplish increasingly complex tasks. If you have a good grasp of math and the Python language, you'll quickly get up to speed with Google Cloud Platform, whether you want to build an AI assistant or a simple business AI application.
• Learn key concepts for data science, machine learning, and deep learning
• Explore tools like Video AI and AutoML Tables
• Build a simple language processor using deep learning systems
• Perform image recognition using CNNs, transfer learning, and GANs
• Use Google's Dialogflow to create chatbots and conversational AI
• Analyze video with automatic video indexing, face detection, and TensorFlow Hub
• Build a complete working AI agent application
Author(s): Micheal Lanham
Edition: 1
Publisher: O'Reilly Media
Year: 2020
Language: English
Commentary: Publisher's PDF
Pages: 391
City: Sebastopol, CA
Tags: Google Cloud Platform; Deep Learning; Data Science; Image Analysis; Python; Chatbots; Convolutional Neural Networks; Recurrent Neural Networks; Autoencoders; Generative Adversarial Networks; Transfer Learning; Image Recognition; Monetization; Natural Language Understanting; Image Classification; Image Generation; Google Colaboratory; You Only Look Once; BERT; Video Analysis
Copyright
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
Conventions Used in This Book
Using Code Examples
O’Reilly Online Learning
How to Contact Us
Acknowledgments
Chapter 1. Data Science and Deep Learning
What Is Data Science?
Classification and Regression
Regression
Goodness of Fit
Classification with Logistic Regression
Multivariant Regression and Classification
Data Discovery and Preparation
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
Optimizing a Deep Learning Network
Overfitting and Underfitting
Network Capacity
Conclusion
Game Answers
Chapter 2. AI on the Google Cloud Platform
AI Services on GCP
The AI Hub
AI Platform
AI Building Blocks
Google Colab Notebooks
Building a Regression Model with Colab
AutoML Tables
The Cloud Shell
Managing Cloud Data
Conclusion
Chapter 3. Image Analysis and Recognition on the Cloud
Deep Learning with Images
Enter Convolution Neural Networks
Image Classification
Set Up and Load Data
Inspecting Image Data
Channels and CNN
Building the Model
Training the AI Fashionista to Discern Fashions
Improving Fashionista AI 2.0
Transfer Learning Images
Identifying Cats or Dogs
Transfer Learning a Keras Application Model
Training Transfer Learning
Retraining a Better Base Model
Object Detection and the Object Detection Hub API
YOLO for Object Detection
Generating Images with GANs
Conclusion
Chapter 4. Understanding Language on the Cloud
Natural Language Processing, with Embeddings
Understanding One-Hot Encoding
Vocabulary and Bag-of-Words
Word Embeddings
Understanding and Visualizing Embeddings
Recurrent Networks for NLP
Recurrent Networks for Memory
Classifying Movie Reviews
RNN Variations
Neural Translation and the Translation API
Sequence-to-Sequence Learning
Translation API
AutoML Translation
Natural Language API
BERT: Bidirectional Encoder Representations from Transformers
Semantic Analysis with BERT
Document Matching with BERT
BERT for General Text Analysis
Conclusion
Chapter 5. Chatbots and Conversational AI
Building Chatbots with Python
Developing Goal-Oriented Chatbots with Dialogflow
Building Text Transformers
Loading and Preparing Data
Understanding Attention
Masking and the Transformer
Encoding and Decoding the Sequence
Training Conversational Chatbots
Compiling and Training the Model
Evaluation and Prediction
Using Transformer for Conversational Chatbots
Conclusion
Chapter 6. Video Analysis on the Cloud
Downloading Video with Python
Video AI and Video Indexing
Building a Webcam Face Detector
Understanding Face Embeddings
Recognizing Actions with TF Hub
Exploring the Video Intelligence API
Conclusion
Chapter 7. Generators in the Cloud
Unsupervised Learning with Autoencoders
Mapping the Latent Space with VAE
Generative Adversarial Network
Exploring the World of Generators
A Path for Exploring GANs
Translating Images with Pix2Pix and CycleGAN
Attention and the Self-Attention GAN
Understanding Self-Attention
Self-Attention for Image Colorization—DeOldify
Conclusion
Chapter 8. Building AI Assistants in the Cloud
Needing Smarter Agents
Introducing Reinforcement Learning
Multiarm Bandits and a Single State
Adding Quality and Q Learning
Exploration Versus Exploitation
Understanding Temporal Difference Learning
Building an Example Agent with Expected SARSA
Using SARSA to Drive a Taxi
Learning State Hierarchies with Hierarchical Reinforcement Learning
Bringing Deep to Reinforcement Learning
Deep Q Learning
Optimizing Policy with Policy Gradient Methods
Conclusion
Chapter 9. Putting AI Assistants to Work
Designing an Eat/No Eat AI
Selecting and Preparing Data for the AI
Training the Nutritionist Model
Optimizing Deep Reinforcement Learning
Building the Eat/No Eat Agent
Testing the AI Agent
Commercializing the AI Agent
Identifying App/AI Issues
Involving Users and Progressing Development
Future Considerations
Conclusion
Chapter 10. Commercializing AI
The Ethics of Commercializing AI
Packaging Up the Eat/No Eat App
Reviewing Options for Deployment
Deploying to GitHub
Deploying with Google Cloud Deploy
Exploring the Future of Practical AI
Conclusion
Index
About the Author
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