Module 05: Computer Vision
Computer vision is the discipline that teaches machines to interpret visual data. It underpins autonomous vehicles, medical imaging, satellite analysis, manufacturing quality control, and any product with a camera. Even if you never build a pure vision system, the architectures and training techniques developed for images have propagated into NLP, audio, and multimodal AI - residual connections, attention mechanisms, and transfer learning all have roots in vision research.
This module takes you from the mechanics of the convolution operation through production-ready architectures, detection and segmentation tasks, and finally to the Vision Transformer - the architecture that unified the vision and language worlds.
Why Computer Vision Matters for ML Engineers
Vision is where deep learning first clearly surpassed humans on a benchmark (ImageNet 2012). The techniques developed for vision have since become universal ML infrastructure:
- Residual connections from ResNet are inside every modern language model
- Attention mechanisms in ViT preceded their widespread use in multimodal systems
- Transfer learning workflows were proven at scale on ImageNet before being applied to every domain
- Data augmentation strategies developed for images are now standard across tabular, text, and audio tasks
A strong computer vision foundation is not optional for ML engineers - it is the historical and technical bedrock of the field.
Module Map
Lesson Table
| # | Lesson | Key Concepts | PyTorch APIs |
|---|---|---|---|
| 01 | Convolutional Neural Networks | Convolution operation, weight sharing, 1x1 convolutions, depthwise separable | nn.Conv2d, filter visualization |
| 02 | Pooling, Strides, and Padding | Max/avg pooling, strided convolutions, receptive field, global avg pooling | nn.MaxPool2d, nn.AdaptiveAvgPool2d |
| 03 | CNN Architectures: ResNet, EfficientNet | Residual blocks, skip connections, compound scaling, ConvNeXt | torchvision.models, custom heads |
| 04 | Transfer Learning | Fine-tuning strategies, layer freezing, domain gap, LR scheduling | requires_grad, pretrained models |
| 05 | Object Detection: YOLO and R-CNN | Bounding boxes, anchor boxes, RPN, NMS, mAP@50 | torchvision.ops, detection models |
| 06 | Semantic Segmentation | FCN, U-Net, skip connections, DeepLab, panoptic segmentation | U-Net from scratch |
| 07 | Data Augmentation | Spatial/photometric transforms, Mixup, CutMix, RandAugment, TTA | torchvision.transforms, Albumentations |
| 08 | Vision Transformers (ViT) | Patch embeddings, CLS token, Swin Transformer, DeiT | timm library, ViT fine-tuning |
Prerequisites
Before this module you should be comfortable with:
- Module 04 - Neural Networks: forward pass, backpropagation, batch norm, dropout, training loops in PyTorch
- NumPy array indexing and reshaping
- Understanding of gradient descent and cross-entropy loss
Production Reality
When you deploy vision systems, the real constraints look like this:
Latency vs accuracy. EfficientNet-B0 runs at ~6ms per image on a V100; ViT-L runs at ~50ms. A real-time 30fps application has ~33ms end-to-end budget. Architecture selection is constrained by inference latency before validation accuracy.
Distribution shift. A model trained on ImageNet-scale web images degrades when deployed on industrial cameras with different optics, color profiles, and lighting conditions. Transfer learning helps, but domain-specific data often determines whether you hit 70% or 95% accuracy.
Label cost. Pixel-level annotation for segmentation costs roughly 60x more per image than classification labels. Semi-supervised and self-supervised pretraining (DINO, MAE) are production tools that every CV engineer should know.
Edge deployment. Phones, cameras, and embedded chips impose hard model-size constraints. MobileNet, EfficientNet-Lite, and quantization-aware training are production-critical knowledge, not optional extras.
How to Study This Module
- Read each lesson in order - concepts build sequentially from CNNs through ViT
- Run every PyTorch code block in a Jupyter notebook or Colab
- For architecture lessons, read the original papers linked in each lesson
- After object detection and segmentation, experiment on a real dataset (COCO, Pascal VOC, Cityscapes)
- Practice the Interview Q&A sections aloud - being able to explain these concepts verbally is what interviews test
This module prepares you for ML Engineer, Computer Vision Engineer, and Applied Scientist roles at companies ranging from startups using pretrained models to research teams designing new architectures.