Syllabus

Weeks Topics Lectures Presentation Topics
Week-1 Neural Network Basics, Multilayer Perceptron, Linear Classifiers, Loss calculation, Log likelihood loss, Cross Entropy Loss, Softmax Classifier, Different Activation Functions and their Derivatives 2
Week-2 Gradient Descent, Chain Rule for Derivatives, Back Propagation, Update Rule, Implementation of Multilayer Perceptron from Scratch that uses back propagation 2
Week-3 Convolutional Neural Network, Filters, Kernels, Convolutional Layer, Max Pool Layer, Activation Function ReLU, Batch Normalization, Implementation of CNN from Scratch 2
Week-4 Capacity, Overfitting, Under fitting, Regularization, Weight Decay, Dropout, Batch Normalization, Convolutional AutoEncoder, Semantic Segmentation, Different up-sampling method (Deconvolution, Reverse Maxpool) 2 Presentation: Semantic Segmentation Presentation
1. Segnet
2. FCN-8
Week-5 Attention, Where CNN pays attention for classification Concept:
Class Activation Map (CAM)		 2 1. GradCAM
Learn to Pay Attention
Week-6 Object Detection, Object localization , Region Proposal, Regional Convolutional Neural Network (R-CNN) , Mask R-CNN 2 1. YOLO
2.Fast R-CNN
3.Faster R-CNN
Week-7 Word Embedding, Word2vec, Negative Sampling, Character Level Embedding, Sentence Level Embedding 2 1. Attention all you need
2. BERT
Week-8 LSTM/GRU for language model, Neural Machine Translation, LST/GRU + Attention, Image Captioning 2 1. Show, Attend, and Tell
Week-9 Self-Attention, Transformer for Neural Machine Translation 2 1. Transformer-XL
Week-10 Introduction to Graph Embedding, Node2vec, Graph Convolution Network 2 1. Representation Learning on Graphs: Method and Application
Week-11 Graph Neural Network (GNN) style Embedding, Graph Attention Network (GAT) style embedding 2 1. GraphSage
Week-12 Advanced Topics Variational Auto Encoder, Generative Adversarial Network, Few/Zero Shot Learning 2