Computer Vision

Computer Vision (CV) enables machines to interpret and understand visual information from images and videos. Deep learning, particularly Convolutional Neural Networks (CNNs), has transformed the field, achieving superhuman performance on many visual tasks.

Core topics include image classification (ResNet, EfficientNet, Vision Transformers), object detection (YOLO, Faster R-CNN), image segmentation (U-Net, Mask R-CNN), and specialized applications like medical imaging, autonomous driving, and visual search.

Key techniques include transfer learning (using pretrained models), data augmentation, self-supervised learning (SimCLR, DINO), and multi-modal models (CLIP, combining vision and language). Understanding CNNs, attention in vision (ViT), and evaluation metrics (mAP, IoU) is essential for ML engineers working with visual data.

Overview

Deep-Dive Concepts (from Projects)

Transfer Learning in Computer Vision

Model Optimization: ONNX and Quantization

Grad-CAM: Model Interpretability

FastAPI for ML Model Serving

Multi-Modal AI Pipelines

Prompt Engineering for Creative AI

Character Consistency in AI Art

Server-Sent Events (SSE)

Professional PDF Generation

AI Content Safety

Contrastive Learning (MoCo)

Self-Distillation (DINO)

Grad-CAM for Multi-Label Classification

Domain Shift in Medical Imaging

Test-Time Adaptation (TENT)

Confidence Calibration