Learning TensorFlow.js - Helion

ebook

Autor: Gant Laborde
ISBN: 9781492090748
stron: 390, Format: ebook
Data wydania: 2021-05-10
Księgarnia: Helion

Cena książki: 152,15 zł (poprzednio: 176,92 zł)
Oszczędzasz: 14% (-24,77 zł)

Osoby, które kupiły tę książkę, wybierały także »

Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde--Google Developer Expert in machine learningand the web--provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers.

You'll begin by working through some basic examples in TensorFlow.js before diving deeper into neural network architectures, DataFrames, TensorFlow Hub, model conversion, transfer learning, and more. Once you finish this book, you'll know how to build and deploy production-readydeep learning systems with TensorFlow.js.

Explore tensors, the most fundamental structure of machine learning
Convert data into tensors and back with a real-world example
Combine AI with the web using TensorFlow.js
Use resources to convert, train, and manage machine learning data
Build and train your own training models from scratch

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Spis treści

Learning TensorFlow.js eBook -- spis treści

Foreword
Preface
- Lets Do This
- Why TensorFlow.js?
- Who Should Read This Book?
- Book Overview
  - The Chapters
  - The Takeaway
- Conventions Used in This Book
- Using Code Examples
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
1. AI Is Magic
- The Path of AI in JavaScript
- What Is Intelligence?
- The History of AI
- The Neural Network
- Todays AI
- Why TensorFlow.js?
  - Significant Support
  - Online Ready
  - Offline Ready
  - Privacy
  - Diversity
- Types of Machine Learning
  - Quick Definition: Supervised Learning
  - Quick Definition: Unsupervised Learning
  - Quick Definition: Semisupervised Learning
  - Quick Definition: Reinforcement Learning
  - Information Overload
- AI Is Everywhere
- A Tour of What Frameworks Provide
  - What Is a Model?
- In This Book
  - Associated Code
    - The extra folder
    - The node folder
    - The simple folder
    - The web folder
  - Chapter Sections
  - Common AI/ML Terminology
    - Training
    - Training set
    - Test set
    - Validation sets
    - Tensors
    - Normalization
    - Data augmentation
    - Features and featurization
- Chapter Review
  - Review Questions
2. Introducing TensorFlow.js
- Hello, TensorFlow.js
- Leveraging TensorFlow.js
- Lets Get TensorFlow.js Ready
- Getting Set Up with TensorFlow.js in the Browser
  - Using NPM
  - Including a Script Tag
- Getting Set Up with TensorFlow.js Node
- Verifying TensorFlow.js Is Working
  - Download and Run These Examples
    - Running the simple example
    - Running the NPM web example
    - Running the Node.js example
- Lets Use Some Real TensorFlow.js
  - The Toxicity Classifier
  - Loading the Model
  - Classifying
- Try It Yourself
- Chapter Review
  - Chapter Challenge: Truck Alert!
  - Review Questions
3. Introducing Tensors
- Why Tensors?
- Hello, Tensors
  - Creating Tensors
  - Tensors for Data Exercises
- Tensors on Tour
  - Tensors Provide Speed
  - Tensors Provide Direct Access
  - Tensors Batch Data
- Tensors in Memory
  - Deallocating Tensors
  - Automatic Tensor Cleanup
- Tensors Come Home
  - Retrieving Tensor Data
- Tensor Manipulation
  - Tensors and Mathematics
  - Recommending Tensors
    - What did you just do?
    - Can you do more?
- Chapter Review
  - Chapter Challenge: What Makes You So Special?
  - Review Questions
4. Image Tensors
- Visual Tensors
- Quick Image Tensors
- JPGs and PNGs and GIFs, Oh My!
  - Browser: Tensor to Image
  - Browser: Image to Tensor
  - Node: Tensor to Image
    - Writing JPGs
    - Writing PNGs
  - Node: Image to Tensor
- Common Image Modifications
  - Mirroring Image Tensors
  - Resizing Image Tensors
  - Cropping Image Tensors
  - New Image Tools
- Chapter Review
  - Chapter Challenge: Sorting Chaos
  - Review Questions
5. Introducing Models
- Loading Models
  - Loading Models Via Public URL
  - Loading Models from Other Locations
    - Browser files
    - Filesystem files
- Our First Consumed Model
  - Loading, Encoding, and Asking a Model
  - Interpreting the Results
  - Cleaning the Board After
- Our First TensorFlow Hub Model
  - Exploring TFHub
  - Wiring Up Inception v3
- Our First Overlayed Model
  - The Localization Model
  - Labeling the Detection
- Chapter Review
  - Chapter Challenge: Cute Faces
  - Review Questions
6. Advanced Models and UI
- MobileNet Again
  - SSD MobileNet
- Bounding Outputs
  - Reading Model Outputs
  - Displaying All Outputs
- Detection Cleanup
  - Quality Checking
  - IoUs and NMS
- Adding Text Overlays
  - Solving Low Contrast
  - Solving Draw Order
- Connecting to a Webcam
  - Moving from Image to Video
  - Activating a Webcam
  - Drawing Detections
- Chapter Review
  - Chapter Challenge: Top Detective
  - Review Questions
7. Model-Making Resources
- Out-of-Network Model Shopping
  - Model Zoos
  - Converting Models
    - Running conversion commands
    - Intermediate models
- Your First Customized Model
  - Meet Teachable Machine
  - Use Teachable Machine
  - Gathering Data and Training
  - Verifying the Model
- Machine Learning Gotchas
  - Small Amounts of Data
  - Poor Data
  - Data Bias
  - Overfitting
  - Underfitting
- Datasets Shopping
  - The Popular Datasets
- Chapter Review
  - Chapter Challenge: R.I.P. You Will Be MNIST
  - Review Questions
8. Training Models
- Training 101
  - Data Prep
  - Design a Model
  - Identify Learning Metrics
  - Task the Model with Training
  - Put It All Together
- Nonlinear Training 101
  - Gathering the Data
  - Adding Activations to Neurons
  - Watching Training
    - Model logs
  - Improving Training
    - Adam optimizer
    - More nodes and layers
- Chapter Review
  - Chapter Challenge: The Model Architect
  - Review Questions
9. Classification Models and Data Analysis
- Classification Models
- The Titanic
  - Titanic Dataset
- Danfo.js
  - Preparing for the Titanic
    - Reading the CSV
    - Investigating the CSV
    - Combining CSVs
    - Cleaning CSVs
    - Saving new CSVs
  - Training on Titanic Data
- Feature Engineering
  - Dnotebook
  - Titanic Visuals
  - Creating Features (aka Preprocessing)
  - Feature Engineered Training Results
  - Reviewing Results
- Chapter Review
  - Chapter Challenge: Ship Happens
  - Review Questions
10. Image Training
- Understanding Convolutions
  - Convolutions Quick Summary
  - Adding Convolution Layers
- Understanding Max Pooling
  - Max Pooling Quick Summary
  - Adding Max Pooling Layers
- Training Image Classification
  - Handling Image Data
- The Sorting Hat
  - Getting Started
  - Converting Folders of Images
  - The CNN Model
  - Training and Saving
- Testing the Model
  - Building a Sketchpad
  - Reading the Sketchpad
- Chapter Review
  - Chapter Challenge: Saving the Magic
  - Review Questions
11. Transfer Learning
- How Does Transfer Learning Work?
  - Transfer Learning Neural Networks
- Easy MobileNet Transfer Learning
  - TensorFlow Hub Check, Mate!
    - Loading chess images
    - Loading the feature model
    - Creating your neural network
    - Training results
- Utilizing Layers Models for Transfer Learning
  - Shaving Layers on MobileNet
  - Layers Feature Model
  - A Unified Model
- No Training Needed
  - Easy KNN: Bunnies Versus Sports Cars
- Chapter Review
  - Chapter Challenge: Warp-Speed Learning
  - Review Questions
12. Dicify: Capstone Project
- A Dicey Challenge
- The Plan
  - The Data
  - The Training
  - The Website
- Generating Training Data
- Training
- The Site Interface
  - Cut into Dice
  - Reconstruct the Image
- Chapter Review
  - Chapter Challenge: Easy as 01, 10, 11
  - Review Questions
Afterword
- Social
- More Books
- Other Options
- More TensorFlow.js Code
- Thanks
A. Chapter Review Answers
- Chapter 1: AI Is Magic
- Chapter 2: Introducing TensorFlow.js
- Chapter 3: Introducing Tensors
- Chapter 4: Image Tensors
- Chapter 5: Introducing Models
- Chapter 6: Advanced Models and UI
- Chapter 7: Model-Making Resources
- Chapter 8: Training Models
- Chapter 9: Classification Models and Data Analysis
- Chapter 10: Image Training
- Chapter 11: Transfer Learning
- Chapter 12: Dicify: Capstone Project
B. Chapter Challenge Answers
- Chapter 2: Truck Alert!
- Chapter 3: What Makes You So Special?
- Chapter 4: Sorting Chaos
- Chapter 5: Cute Faces
- Chapter 6: Top Detective
- Chapter 7: R.I.P. You will be MNIST
- Chapter 8: The Model Architect
- Chapter 9: Ship Happens
- Chapter 10: Saving the Magic
- Chapter 11: Warp-Speed Learning
- Chapter 12: Easy as 01, 10, 11
C. Rights and Licenses
- Unsplash License
- Apache License 2.0
- Public Domain
- WTFPL
- Creative Commons Attribution-sharealike 4.0 International License (CC BY-SA 4.0)
- Creative Commons Attribution 4.0 International License (CC BY 4.0)
- Gant Laborde and OReilly
- TensorFlow and TensorFlow.js Logos
Index