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