From Zero to Hero: Mastering Neural Networks with Keras

Introduced to Colab by Google

How to Implement Deep Neural Network

How to Implement Convolutional Neural Network

How to Implement Recurrent Neural Network

How to Implement Complex Neural Network which has both CNN and RNN layers


  • Student should know theoretical concepts of Deep Learning
  • Some experience with Python will be a plus


In this comprehensive course, you will learn how to implement various types of neural networks using Keras, with step-by-step guidance and hands-on projects. You don’t need to set up anything on your system as everything will be done online. You will be provided with example code and practice problems to reinforce your understanding of the concepts.

Throughout the course, you will work on four exciting projects that cover different neural network architectures and datasets. You will start by implementing and training a fully connected neural network for character classification using the popular MNIST dataset. You will then move on to creating and training a convolutional neural network (CNN) for the same dataset.

Next, you will learn how to implement and train a multi-layer LSTM neural network for Human Activity Recognition using the WISDM dataset. Finally, you will explore how to build and train a multi-layer CNN-RNN neural network for the same dataset.

For each project, you will be provided with code and Colab notebooks to experiment with, allowing you to practice and apply what you have learned in a real-world setting. This course is designed to take you from the basics to advanced models, so you can develop your skills and confidently implement complex neural networks.

While a theoretical background in deep learning is expected, a basic understanding is sufficient to get started with this course. Join us now and learn how to build and train neural networks using Keras!

Who this course is for:

  • Beginners course for people interested in learning the implementation of Neural Networks and doing real world projects

Course content

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