Doctoral Research Proposal
Deep Learning
- DL00 - The whole idea behind Convolutional Neural Networks (CNNs)
- DL01 - Activation Functions, Optimization Methods, and Loss Functions
- DL02 - Deeper CNN Variants and their Architecture
- DL03 - Recurrent Neural Networks(RNNs) and difficulty in training
- DL04 - LSTM and GRU
- DL05 - Encoder-Decoder, Attention Mechanism and Transformers
- DL06 - Generative Adversarial Networks(GANs) and its variants
- DL07 - AutoEncoder and its variants
Self-Supervised Learning
- SSL01 - Self-Supervised Learning - CPC, Deep InfoMax, SimCLR
- SSL02 - Self-Supervised Learning - MoCo, BYOL, SwAV, SimSiam
Federated Learning
Computer Vision
- CV00 - Basics of Image Processing
- CV01 - Image Transformation
- CV02 - Feature Detection and Matching
- CV03 - Object Detection Models - Two-stage Detectors (SS, RCNN based)
- CV04 - Object Detection Models - One-stage Dectectors (YOLO, RetinaNet)
- CV05 - Evaluation Metrics used in CV problems
- CV06 - Image Segmentation - UNet, MaskRCNN
Reinforcement Learning
- RL00 - Markov Decision Process (MDP)
- RL01 - Policies and Value Function
- RL02 - Monte Carlo Methods and Control
- RL03 - Off-Policy via Importance Sampling
- RL04 - TD Learning
- RL05 - SARSA and Q-Learning
- RL06 - n-step Bootstrapping
- RL07 - Deep Q-Learning and Double Q-Learning
- RL08 - Policy Gradient Methods & REINFORCE
Statistics Essentials