Basic Concept of Neurons – Perceptron Algorithm – Single Layer Neural Network and Multilayer Neural Network- Feed Forward Neural Network and Back propagation Networks- Create and deploy neural networks using Tensor Flow for Image data.

Deep Neural Networks – Gradient Descent –Differentiation Algorithms – Vanishing Gradient Problem – Mitigation – Rectified Linear Unit (ReLU) – Heuristics for Avoiding Bad Local Minima – Heuristics for Faster Training – Nestors Accelerated Gradient Descent

CNN Architectures – Convolution Layer– Pooling Layers –Hyper parameter–Activation Function–Recurrent and Recursive Nets – Recurrent Neural Networks – Deep Recurrent Networks – Recursive Neural Networks – Create and deploy Convolutional Neural networks using Keras for Image data

Long Short Term Memory (LSTM) Networks – Sequence Prediction – Gated Recurrent – Encoder/Decoder Architectures – Autoencoders – Standard – Sparse – Denoising – Contractive – VariationalAutoencoders – Applications of Autoencoders – Case Study: Representation Learning

mages segmentation – Object Detection – Automatic Image Captioning – Image generation with Generative adversarial networks – Video to Text with LSTM models – Attention models for Computer Vision. Case Study: Named Entity Recognition – Opinion Mining using Recurrent Neural Networks – Parsing and Sentiment Analysis using Recursive Neural Networks