Overview

Build computer vision systems that actually see. Build computer vision systems that work. Create models used in apps like self-driving cars and medical imaging.

Convolutions and Pooling

Teach models to "see" edges and shapes

Convolutions slide over pixels to find patterns automatically. Your project uses these exact operations.

You'll learn: How filters detect edges/textures, stride/padding control, pooling to focus on important areas

Convolutional Neural Networks

Complete image classifiers from scratch

Stack conv → pool → dense layers into working CNNs. See your classification project come alive.

You'll learn: Full CNN pipeline, feature extraction step-by-step, why CNNs beat regular networks for images

Data Augmentation

Turn 100 photos into 10,000 training examples

Flip, rotate, zoom - make models ready for the real world. Instant accuracy boost for your CNN project.

You'll learn: Smart transforms that preserve labels, augmentation pipelines, balancing strength

Deep Convolutional Architectures

Scale to 100M+ parameter vision models

ResNet connections + VGG blocks = production CNNs. Build the deep networks from your project.

You'll learn: Residual blocks that train deep, depth vs width, modern ImageNet winners

Transfer Learning

Borrow years of training in 5 minutes

Start with ImageNet-pretrained models. Fine-tune for your data - the smart way to build vision apps.

You'll learn: Freeze/extract/fine-tune strategies, avoiding common transfer pitfalls

Object Detection

Draw boxes around every object

Find where AND what is in images. Your project's anchors + NMS = production object detection.

You'll learn: Bounding boxes, IoU matching, non-max suppression, mAP evaluation

Real vision systems = CNN engineering. Right data + architecture + validation = models that work on your phone camera tomorrow.