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Extensive Vision AI Program

Phase 1:

  • 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

Phase 2

  • 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!

Phase 3

  • 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

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