Syllabus Neural Networks and Deep Learning - [CCS355] UNIT I INTRODUCTION Neural Networks-Application Scope of Neural Networks - Artificial Neural Network : An Introduction - Evolution of Neural Networks - Basic Models of Artificial Neural Network- Important Terminologies of ANNs - Supervised Learning Network. (Chapter - 1) UNIT II ASSOCIATIVE MEMORY AND UNSUPERVISED LEARNING NETWORKS Training Algorithms for Pattern Association-Autoassociative Memory Network-Heteroassociative Memory Network-Bidirectional Associative Memory (BAM) - Hopfield Networks - Iterative Autoassociative Memory Networks - Temporal Associative Memory Network-Fixed Weight Competitive Nets-Kohonen Self - Organizing Feature Maps-Learning Vector Quantization-Counter propagation Networks-Adaptive Resonance Theory Network. (Chapter - 2) UNIT III THIRD-GENERATION NEURAL NETWORKS Spiking Neural Networks - Convolutional Neural Networks-Deep Learning Neural Networks-Extreme Learning Machine Model - Convolutional Neural Networks : The Convolution Operation - Motivation - Pooling - Variants of the basic Convolution Function - Structured Outputs - Data Types - Efficient Convolution Algorithms - Neuroscientific Basis - Applications : Computer Vision, Image Generation, Image Compression. (Chapter - 3) UNIT IV DEEP FEEDFORWARD NETWORKS History of Deep Learning - A Probabilistic Theory of Deep Learning - Gradient Learning - Chain Rule and Backpropagation - Regularization : Dataset Augmentation - Noise Robustness - Early Stopping, Bagging and Dropout - batch normalization- VC Dimension and Neural Nets. (Chapter - 4) UNIT V RECURRENT NEURAL NETWORKS Recurrent Neural Networks : Introduction - Recursive Neural Networks - Bidirectional RNNs - Deep Recurrent Networks - Applications : Image Generation, Image Compression, Natural Language Processing. Complete Auto encoder, Regularized Autoencoder, Stochastic Encoders and Decoders, Contractive Encoders. (Chapter - 5)