Deep Learning is part of a broader family of machine learning methods based on artificial neural networks.

Deep-learning architectures such as deep neural networks,  recurrent neural networks, convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced good results

Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains.

Keras is the most used deep learning framework. Keras follows best practices for reducing cognitive load: it offers APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages.

Following topics are covered as part of the course

• Explore building blocks of neural networks

  • Data representation, Tensor, Back propagation

    
    

• Keras

  • Dataset, Applying Keras to cases studies, over fitting / under fitting

    
    

• Artificial Neural Networks (ANN)

  • Activation functions

  • Loss functions

  • Gradient Descent

  • Optimizer

    
    

• Image Processing

  • Convnets (CNN), hands-on with CNN

    
    

• Text and Sequences

  • Text data, Language Processing

  • Recurrent Neural Network (RNN)

  • LSTM

  • Bidirectional RNN

• Gradients and Back Propagation - Mathematics

  • Gradient Descent

  • Mathematics

    
    

• Image Processing  / CV - Advanced

  • Image Data Generator

  • Image Data Generator - Data Augmentation

  • Pre-trained network

    
    

• Functional API

  • Intro to Functional API

  • Multi Input Multi Output Model

The videos are concepts and hands-on implementation of topics