Book description
Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach.
Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use.
- Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite
- Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral
- Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies
- Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning
- Use transfer learning to train models in minutes
- Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users
Publisher resources
Table of contents
- Preface
- 1. Exploring the Landscape of Artificial Intelligence
- 2. What’s in the Picture: Image Classification with Keras
- 3. Cats Versus Dogs: Transfer Learning in 30 Lines with Keras
- 4. Building a Reverse Image Search Engine: Understanding Embeddings
- 5. From Novice to Master Predictor: Maximizing Convolutional Neural Network Accuracy
-
6. Maximizing Speed and Performance of TensorFlow: A Handy Checklist
- GPU Starvation
- How to Use This Checklist
- Performance Checklist
- Data Preparation
- Data Reading
- Data Augmentation
-
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
- Install an Optimized Stack for the Hardware
- Optimize the Number of Parallel CPU Threads
- Use Better Hardware
- Distribute Training
- Examine Industry Benchmarks
- Inference
- Summary
- 7. Practical Tools, Tips, and Tricks
- 8. Cloud APIs for Computer Vision: Up and Running in 15 Minutes
- 9. Scalable Inference Serving on Cloud with TensorFlow Serving and KubeFlow
- 10. AI in the Browser with TensorFlow.js and ml5.js
-
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
- Apple’s Machine Learning Architecture
- Building a Real-Time Object Recognition App
- Conversion to Core ML
- Dynamic Model Deployment
- On-Device Training
- Performance Analysis
- Measuring Energy Impact
- Reducing App Size
- Case Studies
- Summary
- 12. Not Hotdog on iOS with Core ML and Create ML
-
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
- Model Conversion to TensorFlow Lite
- Building a Real-Time Object Recognition App
- ML Kit + Firebase
- TensorFlow Lite on iOS
- Performance Optimizations
- 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
- Summary
-
14. Building the Purrfect Cat Locator App with TensorFlow Object Detection API
- Types of Computer-Vision Tasks
- Approaches to Object Detection
- Invoking Prebuilt Cloud-Based Object Detection APIs
- Reusing a Pretrained Model
- Building a Custom Detector Without Any Code
- The Evolution of Object Detection
- Key Terms in Object Detection
- Using the TensorFlow Object Detection API to Build Custom Models
- Inspecting the Model
- Image Segmentation
- Case Studies
- Summary
- 15. Becoming a Maker: Exploring Embedded AI at the Edge
-
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
- Data Exploration and Preparation
- Training Our Autonomous Driving Model
- Deploying Our Autonomous Driving Model
- Further Exploration
- Summary
- 17. Building an Autonomous Car in Under an Hour: Reinforcement Learning with AWS DeepRacer
- A Crash Course in Convolutional Neural Networks
- Index
Product information
- Title: Practical Deep Learning for Cloud, Mobile, and Edge
- Author(s):
- Release date: October 2019
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492034865
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