Practical Deep Learning with Keras and Python

Video description

Learn to apply machine learning to your problems. Follow a complete pipeline including pre-processing and training.

About This Video

  • Run deep learning models with Keras on a TensorFlow backend
  • Understand how to feed your own data to deep learning models (that is, handling the notorious shape mismatch issue)
  • Understand Deep Learning with minimal math
  • Understand and code Convolutional Neural Networks as well as graph-based deep models involving residual connections and inception modules
  • Understand and use Keras' functional API to create models with multiple inputs and outputs

In Detail

This course is for you if you are new to Machine Learning but want to learn it without all the math. This course is also for you if you have tried to use a machine learning course but could never figure out how to use it to solve your own problems.

In this course, we will start from scratch. So we will immediately start coding even before installation! You will see a brief bit of absolutely essential theory and then we will get into environment setup and explain almost all concepts through code. You will be using Keras, one of the easiest and most powerful machine learning tools out there.

You will start with a basic model of how machines learn and then move on to higher models, such as:

  • Convolutional Neural Networks
  • Residual connections
  • Google's Inception Module

All this with only a few lines of code. All the examples used in the course come with starter code which will get you started and without the hard work.

All the code files are placed at

Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at If you purchased this course elsewhere, you can visit and register to have the files e-mailed directly to you.

Product information

  • Title: Practical Deep Learning with Keras and Python
  • Author(s): Dr. Mohammad Nauman
  • Release date: December 2018
  • Publisher(s): Packt Publishing
  • ISBN: 9781838554729