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Intelligent Mobile Projects with TensorFlow

  • Course Code: Data Science - Intelligent Mobile Projects with TensorFlow
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for those who wants to Create Deep Learning and Reinforcement Learning apps for multiple platforms with TensorFlow.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Intelligent Mobile Projects with TensorFlow skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Create Deep Learning and Reinforcement Learning apps for multiple platforms with TensorFlow. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

As a developer, you always need to keep an eye out and be ready for what will be trending soon, while also focusing on what’s trending currently. So, what’s better than learning about the integration of the best of both worlds, the present and the future? Artificial Intelligence (AI) is widely regarded as the next big thing after mobile, and Google’s TensorFlow is the leading open source machine learning framework, the hottest branch of AI. This course covers more than 10 complete iOS, Android, and Raspberry Pi apps powered by TensorFlow and built from scratch, running all kinds of cool TensorFlow models offline on-device: from computer vision, speech and language processing to generative adversarial networks and AlphaZero-like deep reinforcement learning. You’ll learn how to use or retrain existing TensorFlow models, build your own models, and develop intelligent mobile apps running those TensorFlow models. You’ll learn how to quickly build such apps with step-by-step tutorials and how to avoid many pitfalls in the process with lots of hard-earned troubleshooting tips.. 

Working in a hands-on learning environment, led by our Intelligent Mobile Projects with TensorFlow expert instructor, students will learn about and explore: 

  • Build TensorFlow-powered AI applications for mobile and embedded devices 
  • Learn modern AI topics such as computer vision, NLP, and deep reinforcement learning 
  • Get practical insights and exclusive working code not available in the TensorFlow documentation 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Classify images with transfer learning 
  • Detect objects and their locations 
  • Transform pictures with amazing art styles 
  • Understand simple speech commands 
  • Describe images in natural language 
  • Recognize drawing with Convolutional Neural Network and Long Short-Term Memory 
  • Predict stock price with Recurrent Neural Network in TensorFlow and Keras 
  • Generate and enhance images with generative adversarial networks 
  • Build AlphaZero-like mobile game app in TensorFlow and Keras 
  • Use TensorFlow Lite and Core ML on mobile 
  • Develop TensorFlow apps on Raspberry Pi that can move, see, listen, speak, and learn 

Audience & Pre-Requisites 

This course is designed for developers wants to Create Deep Learning and Reinforcement Learning apps for multiple platforms with TensorFlow 

Pre-Requisites:  Students should have familiar with  

  • Basics of Python and ML 
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

  1. Getting Started with Mobile TensorFlow 
  • Getting Started with Mobile TensorFlow 
  • Setting up TensorFlow 
  • Setting up Xcode 
  • Setting up Android Studio 
  • TensorFlow Mobile vs TensorFlow Lite 
  • Running sample TensorFlow iOS apps 
  • Running sample TensorFlow Android apps 
  1. Classifying Images with Transfer Learning 
  • Classifying Images with Transfer Learning 
  • Transfer learning – what and why 
  • Retraining using the Inception v3 model 
  • Retraining using MobileNet models 
  • Using the retrained models in the sample iOS app 
  • Using the retrained models in the sample Android app 
  • Adding TensorFlow to your own iOS app 
  • Adding TensorFlow to your own Android app 
  1. Detecting Objects and Their Locations 
  • Detecting Objects and Their Locations 
  • Object detection–a quick overview 
  • Setting up the TensorFlow Object Detection API 
  • Retraining SSD-MobileNet and Faster RCNN models 
  • Using object detection models in iOS 
  • Using YOLO2–another object-detection model 
  1. Transforming Pictures with Amazing Art Styles 
  • Transforming Pictures with Amazing Art Styles 
  • Neural Style Transfer – a quick overview 
  • Training fast neural-style transfer models 
  • Using fast neural-style transfer models in iOS 
  • Using fast neural-style transfer models in Android 
  • Using the TensorFlow Magenta multi-style model in iOS 
  • Using the TensorFlow Magenta multi-style model in Android 
  1. Understanding Simple Speech Commands 
  • Understanding Simple Speech Commands 
  • Speech recognition – a quick overview 
  • Training a simple commands recognition model 
  • Using a simple speech recognition model in Android 
  • Using a simple speech recognition model in iOS with Objective-C 
  • Using a simple speech recognition model in iOS with Swift 
  1. Describing Images in Natural Language 
  • Describing Images in Natural Language 
  • Image captioning – how it works 
  • Training and freezing an image captioning model 
  • Transforming and optimizing the image captioning model 
  • Using the image captioning model in iOS 
  • Using the image captioning model in Android 
  1. Recognizing Drawing with CNN and LSTM 
  • Recognizing Drawing with CNN and LSTM 
  • Drawing classification – how it works 
  • Training, predicting, and preparing the drawing classification model 
  • Using the drawing classification model in iOS 
  • Using the drawing classification model in Android 
  1. Predicting Stock Price with RNN 
  • Predicting Stock Price with RNN 
  • RNN and stock price prediction – what and how 
  • Using the TensorFlow RNN API for stock price prediction 
  • Using the Keras RNN LSTM API for stock price prediction 
  • Running the TensorFlow and Keras models on iOS 
  • Running the TensorFlow and Keras models on Android 
  1. Generating and Enhancing Images with GAN 
  • Generating and Enhancing Images with GAN 
  • GAN – what and why 
  • Building and training GAN models with TensorFlow 
  • Using the GAN models in iOS 
  • Using the GAN models in Android 
  1. Building an AlphaZero-like Mobile Game App 
  • Building an AlphaZero-like Mobile Game App 
  • AlphaZero – how does it work? 
  • Training and testing an AlphaZero-like model for Connect 4 
  • Using the model in iOS to play Connect 4 
  • Using the model in Android to play Connect 4 
  1. Using TensorFlow Lite and Core ML on Mobile 
  • Using TensorFlow Lite and Core ML on Mobile 
  • TensorFlow Lite – an overview 
  • Using TensorFlow Lite in iOS 
  • Using TensorFlow Lite in Android 
  • Core ML for iOS – an overview 
  • Using Core ML with Scikit-Learn machine learning  
  • Using Core ML with Keras and TensorFlow 
  1. Developing TensorFlow Apps on Raspberry Pi 
  • Developing TensorFlow Apps on Raspberry Pi 
  • Setting up Raspberry Pi and making it move 
  • Setting up TensorFlow on Raspberry Pi 
  • Image recognition and text to speech 
  • Audio recognition and robot movement 
  • Reinforcement learning on Raspberry Pi 
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