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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

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