Deep Learning with TensorFlow 2.0 Training Course

Course Code

tensorflow

Duration

21 hours (usually 3 days including breaks)

Requirements

  • Programming experience in Python.
  • Experience with the Linux command line.

Audience

  • Developers
  • Data Scientists

Overview

TensorFlow is a popular and machine learning library developed by Google for deep learning, numeric computation, and large-scale machine learning. TensorFlow 2.0, released in Jan 2019, is the newest version of TensorFlow and includes improvements in eager execution, compatibility and API consistency.

This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to use Tensorflow 2.0 to build predictors, classifiers, generative models, neural networks and so on.

By the end of this training, participants will be able to:

  • Install and configure TensorFlow 2.0.
  • Understand the benefits of TensorFlow 2.0 over previous versions.
  • Build deep learning models.
  • Implement an advanced image classifier.
  • Deploy a deep learning model to the cloud, mobile and IoT devices.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
  • To learn more about TensorFlow, please visit: https://www.tensorflow.org/

Course Outline

Introduction

  • TensorFlow 2.0 vs previous versions -- What's new

Setting up Tensoflow 2.0

Overview of TensorFlow 2.0 Features and Architecture

How Neural Networks Work

Using TensorFlow 2.0 to Create Deep Learning Models

Analyzing Data

Preprocessing Data

Building a Model

Implementing a State-of-the-Art Image Classifier

Training the Model

Training on a GPU vs a TPU

Evaluating the Model

Making Predictions

Evaluating the Predictions

Debugging the Model

Saving a Model

Deploying a Model to the Cloud

Deploying a Model to a Mobile Device

Deploying a Model to an Embedded System (IoT)

Integrating a Model with Different Languages

Troubleshooting

Summary and Conclusion

Testimonials

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