Keras Deep Learning Training focuses on implementation of Keras for fast and efficient deep-learning models. Neural Network library, Keras, generally deals with network library usually written in Python Programming Language.


  • Understanding the basic understanding of Keras and its implementation
  • Creation and Deployment of Blockchain Application in more secure manner
  • Implementation of Keras in future-scope for better Secure Application


  • Capable of running on top of MXnet, Deeplearning4j
  • Expertise in delivering and deploying Blockchain Application more faster
  • Expertise in deep-learning models and practical use-cases
Duration: 2 Days

Course Content:
  1. Basic Overview of Keras
  2. Understanding the features of Keras
  3. Basic Installation of Keras

    • Installation of dependencies
    • Installation of Theano
    • Installation of TensorFlow
    • Installation of Keras
    • Testing each Installation
  4. Basic Configuration of Keras

    • Installation of Keras on Docker
    • Installation of Keras on Google Cloud ML
    • Installation of Keras on Amazon AWS
    • Installation of Keras on Microsoft Azure
  5. Understanding Keras API

    • Basic Architecture of Keras
    • Overview of predefined neural network layers
    • Overview of predefined activation functions
    • Understanding Losses Functions
    • Understanding Metrics
  6. Overview of Deep Learning with ConvNets

    • Understanding Deep Convolutional Neural Network (DCNN)
    • Simple Example of DCNN
    • Recognizing CIFAR-10 images with DL
  1. Concept of Generative Adversarial Networks and Wavenet

    • Overview of GAN
    • Keras adversarial GANs for forging MNIST
    • Keras adversarial GANs for forging CIFAR
    • Understanding WaveNet
  2. Deep Dive into Word Embeddings

    • Understanding word2vec
    • GloVe Exploring functionalities
    • Using pre-trained embeddings
  3. Overview of Recurrent Neural Networks RNN

    • Basics of SimpleRNN cells
    • Understanding RNN Topologies
    • Vanishing and exploding gradients
    • Using Long Short Term Memory -LSTM
    • Using Gated Recurrent Unit -GRU
    • Concept of Bidirectional RNNs
    • Understanding Stateful RNNs
  4. Additional Deep Learning Models

    • Dealing with Keras Functional API
    • Understanding Regression Networks
    • Concept of Unsupervised Learning
    • Keras Customization scenario
    • Using Generative Models