Learn to build and deploy deep learning models for image classification, object detection, and segmentation using PyTorch and TensorFlow. This course walks from convolutional networks built from scratch through transfer learning with modern backbones, real-time video processing with OpenCV, and production deployment with ONNX and TensorRT. You will finish with a practical workflow for taking a vision idea from dataset to running system.

Modules

Convolutional Neural Networks from Scratch

Lessons

• How Convolutions Turn Pixels into Features Preview
• Pooling Activation and the Anatomy of a CNN
• Training Your First CNN End to End

Transfer Learning with Modern Backbones

Lessons

• Why Pretrained Backbones Beat Training from Zero
• Fine-Tuning ResNet and EfficientNet the Right Way
• Vision Transformers and When to Choose Them

Object Detection in Practice

Lessons

• Framing the Detection Problem and Its Metrics
• One-Stage Detection Speed with YOLO
• Two-Stage Precision and Transformer Detection with DETR

Image Segmentation Techniques

Lessons

• From Boxes to Masks in Semantic and Instance Segmentation
• U-Net and the Encoder-Decoder Pattern
• Instance Masks with Mask R-CNN

Real-Time Video with OpenCV

Lessons

• OpenCV Fundamentals for Image Handling
• Building a Real-Time Video Inference Pipeline
• Classical Vision Tools That Still Earn Their Keep

Deploying Vision Models to Production

Lessons

• Exporting Models with ONNX
• Accelerating Inference with TensorRT
• Serving Monitoring and Maintaining Vision Models