Deep Learning & Computer Vision Notes
Subject Overview
Year 2 (Deep Learning & Language) moves into neural networks — how modern AI actually works under the hood, from a single neuron to transformers. This semester's goal is to understand neural networks deeply enough to design, train and debug them for vision tasks: fundamentals and PyTorch, training technique, convolutional networks, transfer learning, and advanced computer vision.
Unit-wise Syllabus
8 units — click WhatsApp below to get the full notes for each
Unit 1: Neural Network Fundamentals (3 hours)
Perceptrons, activation functions, forward and backward propagation, building a neural network from scratch in NumPy
Unit 2: PyTorch Essentials (3 hours)
Tensors, autograd, nn.Module, datasets and dataloaders, training loops, why PyTorch dominates research
Unit 3: Training Deep Networks (3 hours)
Optimizers (SGD, Adam), learning rate schedules, regularization, batch norm, dropout, debugging training
Unit 4: Convolutional Neural Networks (4 hours)
Convolutions, pooling, classic architectures (LeNet, ResNet, EfficientNet), image classification end-to-end
Unit 5: Transfer Learning & Fine-tuning (3 hours)
Using pretrained models from Hugging Face and torchvision, feature extraction versus fine-tuning
Unit 6: Advanced CV Tasks (4 hours)
Object detection (YOLO), segmentation (U-Net, SAM), image generation basics and diffusion intuition
Unit 7: Tools introduced
PyTorch, Hugging Face transformers and datasets, Google Colab Pro / Kaggle GPUs for training, Roboflow for dataset preparation, Cursor with Claude in agentic mode for refactoring training code
Unit 8: Mini-projects
Build a CNN from scratch to classify CIFAR-10, fine-tune a ResNet for a custom domain (e.g. plant disease detection), deploy an object detector to a webcam feed using YOLOv8