Practical Deep Learning for Cloud, Mobile, and Edge. Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow

Spis treści książki

• Preface
  • To the Backend/Frontend/Mobile Software Developer
  • To the Data Scientist
  • To the Student
  • To the Teacher
  • To the Robotics Enthusiast
  • What to Expect in Each Chapter
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
    • Group Acknowledgments
    • Personal Acknowledgments
• 1. Exploring the Landscape of Artificial Intelligence
  • An Apology
  • The Real Introduction
  • What Is AI?
    • Motivating Examples
  • A Brief History of AI
    • Exciting Beginnings
    • The Cold and Dark Days
    • A Glimmer of Hope
    • How Deep Learning Became a Thing
  • Recipe for the Perfect Deep Learning Solution
    • Datasets
    • Model Architecture
    • Frameworks
      • TensorFlow
      • Keras
      • PyTorch
      • A continuously evolving landscape
    • Hardware
  • Responsible AI
    • Bias
    • Accountability and Explainability
    • Reproducibility
    • Robustness
    • Privacy
  • Summary
  • Frequently Asked Questions
• 2. Whats in the Picture: Image Classification with Keras
  • Introducing Keras
  • Predicting an Images Category
  • Investigating the Model
    • ImageNet Dataset
    • Model Zoos
    • Class Activation Maps
  • Summary
• 3. Cats Versus Dogs: Transfer Learning in 30 Lines with Keras
  • Adapting Pretrained Models to New Tasks
    • A Shallow Dive into Convolutional Neural Networks
    • Transfer Learning
    • Fine Tuning
    • How Much to Fine Tune
  • Building a Custom Classifier in Keras with Transfer Learning
  • Organize the Data
  • Build the Data Pipeline
    • Number of Classes
      • Binary classification
      • Multiclass classification
    • Batch Size
  • Data Augmentation
  • Model Definition
  • Train the Model
    • Set Training Parameters
    • Start Training
  • Test the Model
  • Analyzing the Results
  • Further Reading
  • Summary
• 4. Building a Reverse Image Search Engine: Understanding Embeddings
  • Image Similarity
  • Feature Extraction
  • Similarity Search
  • Visualizing Image Clusters with t-SNE
  • Improving the Speed of Similarity Search
    • Length of Feature Vectors
    • Reducing Feature-Length with PCA
  • Scaling Similarity Search with Approximate Nearest Neighbors
    • Approximate Nearest-Neighbor Benchmark
    • Which Library Should I Use?
    • Creating a Synthetic Dataset
    • Brute Force
    • Annoy
    • NGT
    • Faiss
  • Improving Accuracy with Fine Tuning
    • Fine Tuning Without Fully Connected Layers
  • Siamese Networks for One-Shot Face Verification
  • Case Studies
    • Flickr
    • Pinterest
    • Celebrity Doppelgangers
    • Spotify
    • Image Captioning
  • Summary
• 5. From Novice to Master Predictor: Maximizing Convolutional Neural Network Accuracy
  • Tools of the Trade
    • TensorFlow Datasets
    • TensorBoard
    • What-If Tool
    • tf-explain
  • Common Techniques for Machine Learning Experimentation
    • Data Inspection
    • Breaking the Data: Train, Validation, Test
    • Early Stopping
    • Reproducible Experiments
  • End-to-End Deep Learning Example Pipeline
    • Basic Transfer Learning Pipeline
    • Basic Custom Network Pipeline
  • How Hyperparameters Affect Accuracy
    • Transfer Learning Versus Training from Scratch
    • Effect of Number of Layers Fine-Tuned in Transfer Learning
    • Effect of Data Size on Transfer Learning
    • Effect of Learning Rate
    • Effect of Optimizers
    • Effect of Batch Size
    • Effect of Resizing
    • Effect of Change in Aspect Ratio on Transfer Learning
  • Tools to Automate Tuning for Maximum Accuracy
    • Keras Tuner
    • AutoAugment
    • AutoKeras
  • Summary
• 6. Maximizing Speed and Performance of TensorFlow: A Handy Checklist
  • GPU Starvation
    • nvidia-smi
    • TensorFlow Profiler + TensorBoard
  • How to Use This Checklist
  • Performance Checklist
    • Data Preparation
    • Data Reading
    • Data Augmentation
    • Training
    • Inference
  • Data Preparation
    • Store as TFRecords
    • Reduce Size of Input Data
    • Use TensorFlow Datasets
  • Data Reading
    • Use tf.data
    • Prefetch Data
    • Parallelize CPU Processing
    • Parallelize I/O and Processing
    • Enable Nondeterministic Ordering
    • Cache Data
    • Turn on Experimental Optimizations
      • Filter fusion
      • Map and filter fusion
      • Map fusion
    • Autotune Parameter Values
  • Data Augmentation
    • Use GPU for Augmentation
      • tf.image built-in augmentations
      • NVIDIA DALI
  • Training
    • Use Automatic Mixed Precision
    • Use Larger Batch Size
    • Use Multiples of Eight
    • Find the Optimal Learning Rate
    • Use tf.function
    • Overtrain, and Then Generalize
      • Use progressive sampling
      • Use progressive augmentation
      • Use progressive resizing
    • Install an Optimized Stack for the Hardware
    • Optimize the Number of Parallel CPU Threads
    • Use Better Hardware
    • Distribute Training
    • Examine Industry Benchmarks
  • Inference
    • Use an Efficient Model
    • Quantize the Model
    • Prune the Model
    • Use Fused Operations
    • Enable GPU Persistence
  • Summary
• 7. Practical Tools, Tips, and Tricks
  • Installation
  • Training
  • Model
  • Data
  • Privacy
  • Education and Exploration
  • One Last Question
• 8. Cloud APIs for Computer Vision: Up and Running in 15 Minutes
  • The Landscape of Visual Recognition APIs
    • Clarifai
      • Whats unique about this API?
    • Microsoft Cognitive Services
      • Whats unique about this API?
    • Google Cloud Vision
      • Whats unique about this API?
    • Amazon Rekognition
      • Whats unique about this API?
    • IBM Watson Visual Recognition
    • Algorithmia
      • Whats unique about this API?
  • Comparing Visual Recognition APIs
    • Service Offerings
    • Cost
    • Accuracy
    • Bias
  • Getting Up and Running with Cloud APIs
  • Training Our Own Custom Classifier
    • Top Reasons Why Our Classifier Does Not Work Satisfactorily
  • Comparing Custom Classification APIs
  • Performance Tuning for Cloud APIs
    • Effect of Resizing on Image Labeling APIs
    • Effect of Compression on Image Labeling APIs
    • Effect of Compression on OCR APIs
    • Effect of Resizing on OCR APIs
  • Case Studies
    • The New York Times
    • Uber
    • Giphy
    • OmniEarth
    • Photobucket
    • Staples
    • InDro Robotics
  • Summary
• 9. Scalable Inference Serving on Cloud with TensorFlow Serving and KubeFlow
  • Landscape of Serving AI Predictions
  • Flask: Build Your Own Server
    • Making a REST API with Flask
    • Deploying a Keras Model to Flask
    • Pros of Using Flask
    • Cons of Using Flask
  • Desirable Qualities in a Production-Level Serving System
    • High Availability
    • Scalability
    • Low Latency
    • Geographic Availability
    • Failure Handling
    • Monitoring
    • Model Versioning
    • A/B Testing
    • Support for Multiple Machine Learning Libraries
  • Google Cloud ML Engine: A Managed Cloud AI Serving Stack
    • Pros of Using Cloud ML Engine
    • Cons of Using Cloud ML Engine
    • Building a Classification API
  • TensorFlow Serving
    • Installation
  • KubeFlow
    • Pipelines
    • Fairing
    • Installation
  • Price Versus Performance Considerations
    • Cost Analysis of Inference-as-a-Service
    • Cost Analysis of Building Your Own Stack
  • Summary
• 10. AI in the Browser with TensorFlow.js and ml5.js
  • JavaScript-Based Machine Learning Libraries: A Brief History
    • ConvNetJS
    • Keras.js
    • ONNX.js
    • TensorFlow.js
  • TensorFlow.js Architecture
  • Running Pretrained Models Using TensorFlow.js
  • Model Conversion for the Browser
  • Training in the Browser
    • Feature Extraction
    • Data Collection
    • Training
    • GPU Utilization
  • ml5.js
  • PoseNet
  • pix2pix
  • Benchmarking and Practical Considerations
    • Model Size
    • Inference Time
  • Case Studies
    • Semi-Conductor
    • TensorSpace
    • Metacar
    • Airbnbs Photo Classification
    • GAN Lab
  • Summary
• 11. Real-Time Object Classification on iOS with Core ML
  • The Development Life Cycle for Artificial Intelligence on Mobile
  • A Brief History of Core ML
  • Alternatives to Core ML
    • TensorFlow Lite
    • ML Kit
    • Fritz
  • Apples Machine Learning Architecture
    • Domain-Based Frameworks
    • ML Framework
    • ML Performance Primitives
  • Building a Real-Time Object Recognition App
  • Conversion to Core ML
    • Conversion from Keras
    • Conversion from TensorFlow
  • Dynamic Model Deployment
  • On-Device Training
    • Federated Learning
  • Performance Analysis
    • Benchmarking Models on iPhones
  • Measuring Energy Impact
    • Benchmarking Load
  • Reducing App Size
    • Avoid Bundling the Model
    • Use Quantization
    • Use Create ML
  • Case Studies
    • Magic Sudoku
    • Seeing AI
    • HomeCourt
    • InstaSaber + YoPuppet
  • Summary
• 12. Not Hotdog on iOS with Core ML and Create ML
  • Collecting Data
    • Approach 1: Find or Collect a Dataset
    • Approach 2: Fatkun Chrome Browser Extension
    • Approach 3: Web Scraper Using Bing Image Search API
  • Training Our Model
    • Approach 1: Use Web UI-based Tools
    • Approach 2: Use Create ML
    • Approach 3: Fine Tuning Using Keras
  • Model Conversion Using Core ML Tools
  • Building the iOS App
  • Further Exploration
  • Summary
• 13. Shazam for Food: Developing Android Apps with TensorFlow Lite and ML Kit
  • The Life Cycle of a Food Classifier App
  • An Overview of TensorFlow Lite
    • TensorFlow Lite Architecture
  • Model Conversion to TensorFlow Lite
  • Building a Real-Time Object Recognition App
  • ML Kit + Firebase
    • Object Classification in ML Kit
    • Custom Models in ML Kit
    • Hosted Models
      • Accessing a hosted model
      • Uploading a hosted model
    • A/B Testing Hosted Models
      • Measuring an experiment
    • Using the Experiment in Code
  • TensorFlow Lite on iOS
  • Performance Optimizations
    • Quantizing with TensorFlow Lite Converter
    • TensorFlow Model Optimization Toolkit
  • Fritz
  • A Holistic Look at the Mobile AI App Development Cycle
    • How Do I Collect Initial Data?
    • How Do I Label My Data?
    • How Do I Train My Model?
    • How Do I Convert the Model to a Mobile-Friendly Format?
    • How Do I Make my Model Performant?
    • How Do I Build a Great UX for My Users?
    • How Do I Make the Model Available to My Users?
    • How Do I Measure the Success of My Model?
    • How Do I Improve My Model?
    • How Do I Update the Model on My Users Phones?
  • The Self-Evolving Model
  • Case Studies
    • Lose It!
    • Portrait Mode on Pixel 3 Phones
    • Speaker Recognition by Alibaba
    • Face Contours in ML Kit
    • Real-Time Video Segmentation in YouTube Stories
  • Summary
• 14. Building the Purrfect Cat Locator App with TensorFlow Object Detection API
  • Types of Computer-Vision Tasks
    • Classification
    • Localization
    • Detection
    • Segmentation
      • Semantic segmentation
      • Instance-level segmentation
  • Approaches to Object Detection
  • Invoking Prebuilt Cloud-Based Object Detection APIs
  • Reusing a Pretrained Model
    • Obtaining the Model
    • Test Driving Our Model
    • Deploying to a Device
  • Building a Custom Detector Without Any Code
  • The Evolution of Object Detection
    • Performance Considerations
  • Key Terms in Object Detection
    • Intersection over Union
    • Mean Average Precision
    • Non-Maximum Suppression
  • Using the TensorFlow Object Detection API to Build Custom Models
    • Data Collection
    • Labeling the Data
    • Preprocessing the Data
  • Inspecting the Model
    • Training
    • Model Conversion
  • Image Segmentation
  • Case Studies
    • Smart Refrigerator
    • Crowd Counting
      • Wildlife conservation
      • Kumbh Mela
    • Face Detection in Seeing AI
    • Autonomous Cars
  • Summary
• 15. Becoming a Maker: Exploring Embedded AI at the Edge
  • Exploring the Landscape of Embedded AI Devices
    • Raspberry Pi
    • Intel Movidius Neural Compute Stick
    • Google Coral USB Accelerator
    • NVIDIA Jetson Nano
    • FPGA + PYNQ
      • FPGAs
      • PYNQ platform
    • Arduino
  • A Qualitative Comparison of Embedded AI Devices
  • Hands-On with the Raspberry Pi
  • Speeding Up with the Google Coral USB Accelerator
  • Port to NVIDIA Jetson Nano
  • Comparing the Performance of Edge Devices
  • Case Studies
    • JetBot
    • Squatting for Metro Tickets
    • Cucumber Sorter
  • Further Exploration
  • Summary
• 16. Simulating a Self-Driving Car Using End-to-End Deep Learning with Keras
  • A Brief History of Autonomous Driving
  • Deep Learning, Autonomous Driving, and the Data Problem
  • The Hello, World! of Autonomous Driving: Steering Through a Simulated Environment
    • Setup and Requirements
  • Data Exploration and Preparation
    • Identifying the Region of Interest
    • Data Augmentation
    • Dataset Imbalance and Driving Strategies
  • Training Our Autonomous Driving Model
    • Drive Data Generator
    • Model Definition
      • Callbacks
  • Deploying Our Autonomous Driving Model
  • Further Exploration
    • Expanding Our Dataset
    • Training on Sequential Data
    • Reinforcement Learning
  • Summary
• 17. Building an Autonomous Car in Under an Hour: Reinforcement Learning with AWS DeepRacer
  • A Brief Introduction to Reinforcement Learning
  • Why Learn Reinforcement Learning with an Autonomous Car?
  • Practical Deep Reinforcement Learning with DeepRacer
    • Building Our First Reinforcement Learning
    • Step 1: Create Model
    • Step 2: Configure Training
      • Configure the simulation environment
      • Configure the action space
      • Configure reward function
      • Configure stop conditions
    • Step 3: Model Training
    • Step 4: Evaluating the Performance of the Model
  • Reinforcement Learning in Action
    • How Does a Reinforcement Learning System Learn?
    • Reinforcement Learning Theory
      • The Markov decision process
      • Model free versus model based
      • Value based
      • Policy based
      • Policy based or value basedwhy not both?
      • Delayed rewards and discount factor ()
    • Reinforcement Learning Algorithm in AWS DeepRacer
    • Deep Reinforcement Learning Summary with DeepRacer as an Example
    • Step 5: Improving Reinforcement Learning Models
      • Algorithm settings
      • Hyperparameters for the neural network
      • Insights into model training
      • Heatmap visualization
      • Improving the speed of our model
  • Racing the AWS DeepRacer Car
    • Building the Track
    • AWS DeepRacer Single-Turn Track Template
    • Running the Model on AWS DeepRacer
    • Driving the AWS DeepRacer Vehicle Autonomously
      • Sim2Real transfer
  • Further Exploration
    • DeepRacer League
    • Advanced AWS DeepRacer
    • AI Driving Olympics
    • DIY Robocars
    • Roborace
  • Summary
• A. A Crash Course in Convolutional Neural Networks
  • Machine Learning
  • Perceptron
  • Activation Functions
  • Neural Networks
  • Backpropagation
  • Shortcoming of Neural Networks
  • Desired Properties of an Image Classifier
  • Convolution
  • Pooling
  • Structure of a CNN
  • Further Exploration
• Index

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