- Background & Basics: Machine Learning Intuition
- Python: Python 101 for Machine Learning
- DNN Concepts: Convolutions, Pooling Operations & Channels
- PyTorch: Pytorch 101 for Vision Machine Learning
- First Neural Network: Kernels, Activations, and Layers
- Architectural Basics: We go through 9 model iterations together, step-by-step to find the final architecture
- Receptive Fields: The CORE fundamental concept behind EVA Program
- BN, Kernels & Regularization: Mathematics behind Batch Normalization, Kernel Initialization, and Regularization
- Backpropagation and Advance Convolutions: Depthwise, Pixel Shuffle, Dilated, Transpose and others
- Advanced Image Augmentation Techniques: Albumentations, Richman's data augmentation and benchmarks
- DNN Interpretability: Class Activation Maps, the most powerful debugging tool at your disposal
- Advanced Training Concepts: Optimizers, LR Schedules, LR Finder & Loss Functions
- SuperConvergence: Cyclic Learning Rates, One Cycle Policy, and Dawnbench
- ResNets: Training ResNet for TinyImageNet from scratch
- Inception: Understanding Inception and DenseNet Architectures
- YoloV2: Understanding YOLOV2 Loss Function
- YoloV5: Implementing Object Detection Training & Transfer Learning
- RCNN Family: RCNN, Fast-RCNN, FasterRCNN & MaskRCNN
- CapStone: Monocular Depth Estimation and Background/Foreground Extraction
- Deploying over AWS: Train, Dockerize and then deploy your model on AWS.
- MobileNet & Other Edge DNNs: Training a DNN for EDGE Deployment from scratch. Understanding MobileNets and ShuffleNets
- Face Recognition Part 1: Face Detection and Detection Strategies
- OpenCV Refresher and Face Recognition Part 2: Implementing Object Tracking and Stabilization, OpenCV and DLIB, for face recognition and others
- Human Pose Estimation: State of Art HPE and Human Localization
- Super-Resolution/Neural-Style-Transfer: Leveraging Transfer learning for NST and "Dense" models
- Segmentation and Usage in Medical Domain: U-NET, its relatives and usage in Medical Science
- GANs: Generative Adversarial Network and Variants
- Encoders: Auto Encoders and Variational AutoEncoders
- Neural Work Embedding: The Embedding Layer
- Sequence Models: RNNs and LSTMs
- GRU, Attention Mechanism & Transformers: Attention is all you need!
- Reinforcement Learning Basics: Markov Decision Processes, Deterministic, and Stochastic Environments & Bellman Equation
- Q-Learning: Q-Learning, Plan vs Policy Networks, and Environment Models
- Deep Q-Learning & DeepTraffic: Custom Environments, OpenGym, Exploration vs Exploitation, and improvements to DQN
- Deep Reinforcement Learning: Policy Gradients, Dynamic Programming, Policy Evaluations, and Temporal Difference Learning
- Actor-Critic Models: Memory Structures, Gibbs Softmax, Eligibility Traces, and Polyak Averaging
- A3C Models: A3C, A3C optimizations,, and implementation logic
- Deep Deterministic Policy Gradients: DDPG Background, Off-Policy Networks, Continuous Action Spaces, and Replay Buffers
- Twin Delayed DDPG Part 1: Clipped Double-Q Learning, Delayed Policy Updates, and Target Policy Smoothing
- Twin Delayed DDPG Part 2: Full TD3 implementation to make a robot walk, and solve a custom environment