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Home » Technology » Big Data & Data Science » Intelligent Mobile Projects with TensorFlow

Course Skill Level:

Intermediate

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:

    INPWTFL21E09

Who should attend & recommended skills:

Developers with basic Python and machine learning skills

Who should attend & recommended skills

  • This course is designed for developers who want to Create Deep Learning and Reinforcement Learning apps for multiple platforms with TensorFlow
  • Skill-level: Foundation-level Intelligent Mobile Projects with TensorFlow skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)
  • Machine Learning: Basic (1-2 years’ experience)

About this course

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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Intelligent Mobile Projects with TensorFlow expert instructor, students will learn about and explore:
  • Building TensorFlow-powered AI applications for mobile and embedded devices
  • Modern AI topics such as computer vision, NLP, and deep reinforcement learning
  • Practical insights and exclusive working code not available in the TensorFlow documentation
  • Classifying images with transfer learning
  • Detecting objects and their locations
  • Transforming pictures with amazing art styles
  • Understanding simple speech commands
  • Describing images in natural language
  • Recognizing drawing with Convolutional Neural Network and Long Short-Term Memory
  • Predicting stock price with Recurrent Neural Network in TensorFlow and Keras
  • Generating and enhancing images with generative adversarial networks
  • Building AlphaZero-like mobile game app in TensorFlow and Keras
  • Using TensorFlow Lite and Core ML on mobile
  • Developing TensorFlow apps on Raspberry Pi that can move, see, listen, speak, and learn

Course breakdown / modules

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • 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

  • GAN – what and why
  • Building and training GAN models with TensorFlow
  • Using the GAN models in iOS
  • Using the GAN models in Android

  • 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

  • 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

  • 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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