Getting Started with TensorFlow for Deep Learning

Video Description

Apply Deep Learning to different data types and solve real-world problems with TensorFlow

About This Video

  • Take the theory and apply it to create networks to classify sentence polarity, recognize handwritten digits, and then locate objects in an image.
  • Learn the fundamentals of deep learning, to get a strong foundation.
  • Combines an easily understandable explanation of deep learning coupled with a handful of implementations using the TensorFlow package.

In Detail

We will not only get you up-and-running with deep learning, but also equip you with the skills to implement your own neural networks and apply them to the real world.

We will use TensorFlow, an efficient Python library used to create and train our neural networks. You'll learn the skills to implement their architecture quickly and efficiently without having to deal with minutiae.

You can rely on our expert guidance while learning the basic theory, backed up with relevant examples. We provide examples of neural networks, which you can use to highlight the key features. We then build up to more advanced networks. You'll learn to utilize a Convolutional Neural Network to classify images of handwritten text and then take your CNN further to perform object detection and localization in an image.

This course will quickly get you past the fundamentals of TensorFlow; you'll go on to more exciting things such as implementing a variety of image recognition tasks. All the code and this course's supporting files are available on GitHub at -

Product Information

  • Title: Getting Started with TensorFlow for Deep Learning
  • Author(s): Tom Joy
  • Release date: November 2018
  • Publisher(s): Packt Publishing
  • ISBN: 9781788475518