User:Pinakinathc

Deep Learning, Computer Vision, and 3D Geometry

Contents

1. Introduction to Deep Learning
   • Gradient Backpropagation
   • Optimisation (e.g., SGD, Adam, AdamW, RMSProp etc.)
   • Reinforcement Learning
2. Neural Networks Architectures
   • Convolutional neural networks (CNNs).
   • Transformers.
   • Graph neural networks.
   • Graph convolutional neural networks.
3. Training a neural network (start coding yourself).
   • A simple image classifier using CNN.
   • A simple text-based image retrieval using CNN.
   • Using a deep learning framework like PyTorch.
   • Large-scale training of Foundation Models.
4. Object Detection
   • Supervised training methods.
   • Weakly supervised training methods.
   • Using large-scale foundation models.
5. Probability and Information Theory in Deep Learning
   • Variational AutoEncoding (VAEs).
   • Flow-based Models.
   • Diffusion Models.
   • Generative Flow Models.
6. Basic Concepts in 3D Geometry
   • Camera Parameters (e.g., Intrinsics and Extrinsics)
   • Polar Coordinates
   • Generate 3D objects using Signed Distance Fields (SDFs).
   • Generate 3D objects using Neural Radiance Fields (NeRFs).
   • Generate 3D objects using Gaussian Splatting.