DEEP LEARNING
DEEP LEARNING UNIT-I: Mathematical foundations of Deep Learning Scalars, Vectors, Matrices and Tensors, Multiplying Matrices and Vectors, Identity and Inverse Matrices, Linear dependence and span, Norms, Special kinds of matrices and vectors, Trace operations, Eigen value decomposition. UNIT-II: Fundamentals of Deep Learning Anatomy of Neural Networks: Layers, Models, Loss functions and optimizers Training Deep Networks: Cost Functions, Optimizers Types of Deep Neural Networks. UNIT-III: Convolutional Neural Networks Motivation, Convolution Operation, Types of layers, Pooling, LENET5 Architecture. UNIT-IV: Recurrent Neural Networks Architecture of traditional RNN, Types and applications of RNN, Variants of RNNs, Word Embedding using Word2vec UNIT-V: Regularization and Autoencoders Regularization for Deep Learning: L1 and L2, Dropout, Data Augmentation, Early Stopping, Case study on MNIST data Autoencoders: Architecture, Implementation, Denoising Autoencoders, Sparse Autoencoders