CSE424: Neural Network

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Syllabus

WeeksTopicsLecturesPresentation Topics
Week-1Neural Network Basics, Multilayer Perceptron, Linear Classifiers, Loss calculation, Log likelihood loss, Cross Entropy Loss, Softmax Classifier, Different Activation Functions and their Derivatives2
Week-2Gradient Descent, Chain Rule for Derivatives, Back Propagation, Update Rule, Implementation of Multilayer Perceptron from Scratch that uses back propagation2
Week-3Convolutional Neural Network, Filters, Kernels, Convolutional Layer, Max Pool Layer, Activation Function ReLU, Batch Normalization, Implementation of CNN from Scratch2
Week-4Capacity, Overfitting, Under fitting, Regularization, Weight Decay, Dropout, Batch Normalization, Convolutional AutoEncoder, Semantic Segmentation, Different up-sampling method (Deconvolution, Reverse Maxpool)2Presentation: Semantic Segmentation Presentation
1. Segnet
2. FCN-8
Week-5Attention, Where CNN pays attention for classification Concept:
Class Activation Map (CAM)		21. GradCAM
Learn to Pay Attention
Week-6Object Detection, Object localization , Region Proposal, Regional Convolutional Neural Network (R-CNN) , Mask R-CNN21. YOLO
2.Fast R-CNN
3.Faster R-CNN
Week-7Word Embedding, Word2vec, Negative Sampling, Character Level Embedding, Sentence Level Embedding21. Attention all you need
2. BERT
Week-8LSTM/GRU for language model, Neural Machine Translation, LST/GRU + Attention, Image Captioning21. Show, Attend, and Tell
Week-9Self-Attention, Transformer for Neural Machine Translation21. Transformer-XL
Week-10Introduction to Graph Embedding, Node2vec, Graph Convolution Network21. Representation Learning on Graphs: Method and Application
Week-11Graph Neural Network (GNN) style Embedding, Graph Attention Network (GAT) style embedding21. GraphSage
Week-12Advanced Topics Variational Auto Encoder, Generative Adversarial Network, Few/Zero Shot Learning2