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Course Skill Level:

Foundational to Intermediate

Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those wishing to learn key structural changes in TensorFlow 2.0 with basic IT, Linux, and Tensorflow knowledge

Who should attend & recommended skills

  • This course is geared for those who want to get a grip on key structural changes in TensorFlow 2.0.
  • Skill-level: Foundation-level TensorFlow skills for Intermediate skilled team members. This is not a basic class.
  • Students should have:
  • Basic to Intermediate IT Skills and Tensorflow knowledge
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su

About this course

TensorFlow is an end-to-end machine learning platform for experts as well as beginners, and its new version, TensorFlow 2.0 (TF 2.0), improves its simplicity and ease of use. This course will help you understand and utilize the latest TensorFlow features. What’s New in TensorFlow 2.0 starts by focusing on advanced concepts such as the new TensorFlow Keras APIs, eager execution, and efficient distribution strategies that help you to run your machine learning models on multiple GPUs and TPUs. The course then takes you through the process of building data ingestion and training pipelines, and it provides recommendations and best practices for feeding data to models created using the new tf.keras API. You will explore the process of building an inference pipeline using TF Serving and other multi-platform deployments before moving on to explore the newly released AIY, which is essentially do-it-yourself AI. This course delves into the core APIs to help you build unified convolutional and recurrent layers and use TensorBoard to visualize deep learning models using what-if analysis. By the end of the course, you’ll have learned about compatibility between TF 2.0 and TF 1.x and be able to migrate to TF 2.0 smoothly.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our TensorFlow expert instructor, students will learn about and explore:
  • Explore TF, Keras, APIs and strategies to run GPUs, TPUs, and compatible APIs across the TensorFlow ecosystem
  • Learn and implement best practices for building data ingestion pipelines using TF 2.0 APIs
  • Migrate your existing code from TensorFlow 1.x to TensorFlow 2.0 seamlessly
  • Implementing tf.keras APIs in TF 2.0 to build, train, and deploy production-grade models
  • Building models with Keras integration and eager execution
  • Distribution strategies to run models on GPUs and TPUs
  • Performing what-if analysis with TensorBoard across a variety of models
  • Vision Kit, Voice Kit, and the Edge TPU for model deployments
  • Building complex input data pipelines for ingesting large training datasets

Course breakdown / modules

  • Technical requirements
  • What’s new?
  • TF 2.0 installation and setup
  • Using TF 2.0
  • Rich extensions

  • Technical requirements
  • New abstractions in TF 2.0
  • Diving deep into the Keras API
  • Estimators
  • Evaluating TensorFlow graphs

  • Technical requirements
  • Designing and constructing the data pipeline
  • Transforming datasets
  • Feeding the created dataset to the model
  • Examples of complete end-to-end data pipelines
  • Best practices and the performance optimization of a data pipeline in TF 2.0
  • Built-in datasets in TF 2.0

  • Technical requirements
  • Comparing Keras and tf.keras
  • Creating models using tf.keras 2.0
  • Model compilation and training
  • Custom training logic
  • Distributed training
  • TensorBoard

  • Technical requirements
  • Machine learning workflow – the inference phase
  • Model artifact – the SavedModel format
  • Inference on backend servers
  • Inference in the browser
  • Inference on mobile and IoT devices

  • Introduction to TFLit
  • Getting started with TFLite
  • Running TFLite on mobile devices
  • Running TFLite on low-power machines
  • Comparing TFLite and TF
  • AIY

  • Migrating From TensorFlow 1.x to 2.0
  • Major changes in TF 2.
  • Recommended techniques to employ for idiomatic TF 2.0
  • Making code TF 2.0-native
  • Frequently asked questions
  • The future of TF 2.0