Advanced Deep Learning with Keras and Python Training Course


14 hours (usually 2 days including breaks)


  • An understanding of basic linear algebra


  • Software Engineers


Keras is an open source Python neural-network library for the creation of deep learning neural-networks. Keras offers an intuitive set of abstractions, simplifying the development of deep learning neural-networks and models.

This instructor-led, live training (online or onsite) is aimed at software engineers who wish to develop advanced deep learning neural-networks and model using Keras and Python.

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

  • Apply deep learning with supervised or unsupervised learning methods.
  • Develop, train, and implement concurrent neural networks and recurrent neural networks.
  • Use Keras and Python to build deep learning models to solve problems involving images, text, sound, and more.

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.

Course Outline


Keras and Deep Learning Frameworks

  • TensorFlow and Theano back-ends
  • Keras vs Tensorflow

Data and Machine Learning

  • Tabular data, visual data, unstructured data, etc.
  • Unsupervised learning, supervised learning, reinforcement learning, etc.

Preparing the Development Environment

  • Installing and configuring Anaconda
  • Installing Keras with a TensorFlow back-end

Neural Networks in Keras

  • Using Keras functional API to build a network
  • Pre-processing and fitting data
  • Defining a Keras model

Mutiple Input and Output Networks

  • Building two input-networks
  • Representing high-cardinality data
  • Merging layers
  • Extending the two input-network
  • Building neural networks with multiple outputs
  • Solving multiple problems simultaneously

Training and Pre-Training

  • Training models
  • Saving and loading models
  • Using ResNet50 on models


  • Exporting Keras logs
  • Visualizing a computational graph and training progress

Google Cloud

  • Exporting models
  • Uploading Keras models
  • Using a model in Google Cloud

Summary and Conclusion

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