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Practical Deep Learning for Cloud and Mobile

Book Description

With Early Release ebooks, you get books in their earliest form—the author's raw and unedited content as he or she writes—so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released.

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 do I begin? This step-by-step guide teaches you how to build practical deep learning applications for the cloud and mobile 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, CoreML, and TensorFlow Lite and go from zero to a production-quality system quickly.

  • Develop deep learning applications for the desktop, cloud, smartphones, browser, and Raspberry Pi
  • Learn by building examples such as Silicon Valley’s "Not Hotdog," image search engines, and your own mini-autonomous car
  • Use transfer learning to train models in minutes
  • Optimize your apps to run efficiently on different hardware
  • Discover strategies to scale up from a single user to millions
  • Sharpen practical skills for data collection, model interoperability, and model debugging using visualizations
  • Uncover the potential for bias and explore the ethical underpinnings for AI-driven technology

Table of Contents

  1. 1. Image Classification with Keras
    1. Introduction to Keras
      1. Layers of Abstraction
    2. Keras in Practice
      1. Predicting an Image’s Category
    3. Analysis
      1. A Model Zoo in Keras
      2. What Does My Neural Network Think?
    4. Summary
  2. 2. Cats vs Dogs - Transfer Learning in 30 lines with Keras
    1. Transfer Learning
      1. Understanding Different Layers in a CNN in the Context of Transfer Learning
    2. Building a Custom Classifier in Keras with Transfer Learning
      1. Organize the data
      2. Set up the Configuration
      3. Data Augmentation
      4. Model Definition
      5. Train and Test
      6. Test the Model
    3. Analyzing the results
    4. Summary
  3. 3. 15 Minutes to Fame: Up and Running with Cloud APIs
    1. Visual Recognition APIs: An Overview
      1. Clarifai
      2. Microsoft Cognitive Services
      3. Google Cloud Vision
      4. Amazon Rekognition
      5. IBM Watson Visual Recognition
      6. Algorithmia
    2. Visual Recognition APIs: A Comparison
      1. Service Offerings
      2. Cost
      3. Accuracy
    3. Get Up and Running with Cloud APIs
    4. Train your Own Classifier
      1. Top reasons why your classifier does not work satisfactorily
    5. Performance Tuning
      1. Resizing
      2. Compression
    6. Case Studies: Cloud APIs used across Industries
      1. Uber
      2. Giphy
      3. OmniEarth
      4. Photobucket
      5. Staples
      6. InDro Robotics
    7. Summary
  4. 4. Real-time Object Recognition on 1000 objects with Keras & CoreML
    1. Introduction to CoreML
      1. API Frameworks from Apple
    2. Building a Real-Time Object Recognition app
    3. Conversion to CoreML
      1. Conversion from Keras
      2. Conversion from Caffe
      3. Conversion from TensorFlow
    4. Dynamic Model Deployment
    5. Limitations of CoreML
    6. Understanding Performance and Resource Tradeoffs with Various Machine Learning Models
      1. Benchmarking Models on iPhones
      2. Measuring Energy Impact
      3. Benchmarking Load
    7. Case Studies
      1. Magic Sudoku
      2. Seeing AI
    8. Summary
  5. 5. Not Hotdog with Keras & CoreML
    1. Data collection
      1. Approach 1: Find or collect a dataset
      2. Approach 2: Fatkun Chrome browser extension
      3. Approach 3: Web scraper using Bing Image Search API
    2. Training mechanism
      1. Approach 1: Using GUI-based tools
      2. Approach 2: Fine-tune using Keras
    3. Model Conversion with coremltools
    4. Building the iOS App
    5. Summary