Dear Students the Lecture Notes has been uploaded for the following topics:Introduction to machine learning, Linear models-SVMs, Perceptrons, logistic regression, Intro to Neural Nets: What a shallow network computes, Training a network: loss functions, back propagation, stochastic gradient descent, Neural networks as universal function approximates, History of Deep Learning, Back propagation and regularization, batch normalization, VC Dimension, Neural Nets-Deep Vs Shallow Networks, Convolutional Networks, Generative Adversarial Networks (GAN), Semi-supervised Learning, Linear (PCA, LDA), metric learning - Auto encoders, dimensionality reduction in networks, Introduction to Convnet , Architectures – AlexNet, VGG, Inception, ResNet - Training a Convnet, weights initialization
