Practical AI on the Google Cloud Platform

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Autor:: Micheal Lanham

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Wydawnictwo:: O'Reilly Media (Z chęcią przeczytam książkę w języku polskim)
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Stron:: 394
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Do przechowalni

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

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O autorze książki

Micheal Lanham is a proven software and tech innovator with 20 years of experience. During that time, he has developed a broad range of software applications in areas such as games, graphics, web, desktop, engineering, artificial intelligence, GIS, and machine learning applications for a variety of industries as an R&D developer. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development. He was later introduced to Unity and has been an avid developer, consultant, manager, and author of multiple Unity games, graphic projects, and books ever since.

Micheal Lanham - pozostałe książki

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Szczegóły książki

ISBN Ebooka:: 978-14-920-7576-9, 9781492075769
Data wydania ebooka :: 2020-10-20 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@helion.pl.
Język publikacji:: 1
Rozmiar pliku ePub:: 19.8MB
Rozmiar pliku Mobi:: 41MB

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Kategorie:
Sztuczna inteligencja
Programowanie » Programowanie w chmurze
DevOps » Google Cloud

Spis treści książki

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
- OReilly 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
  - 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
    - Game 5
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
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
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
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
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
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
    - Translating unpaired images with CycleGAN
- Attention and the Self-Attention GAN
  - Understanding Self-Attention
  - Self-Attention for Image ColorizationDeOldify
- Conclusion
8. Building AI Assistants in the Cloud
- Needing Smarter Agents
- Introducing Reinforcement Learning
  - Multiarm Bandits and a Single State
    - Choosing the greedy action
  - Adding Quality and Q Learning
    - Playing with OpenAI Gym
  - Exploration Versus Exploitation
    - Balancing exploration and exploitation
  - Understanding Temporal Difference Learning
    - Episodic versus continuous learning
    - On policy versus off policy
    - Model-based versus model-free
    - Discrete versus continuous actions or states
- Building an Example Agent with Expected SARSA
  - Using SARSA to Drive a Taxi
    - Understanding hierarchical states
  - Learning State Hierarchies with Hierarchical Reinforcement Learning
    - Functional decomposition with MAXQ
- Bringing Deep to Reinforcement Learning
  - Deep Q Learning
  - Optimizing Policy with Policy Gradient Methods
- Conclusion
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
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