Convolutional Neural Networks (CNNs) — Deep Learning & Neural Network…

The architecture family that transformed computer vision

Steps in Convolutional Neural Networks (CNNs)

The Convolution Operation — advanced · Filters, kernels, stride and padding, and how convolution detects local patterns
Pooling Layers — advanced · Max and average pooling for downsampling and building translation invariance
Classic CNN Architectures (LeNet, AlexNet, VGG) — advanced · The architectures that established the CNN paradigm for image classification
Modern CNN Architectures (ResNet, EfficientNet) — advanced · Skip connections and compound scaling that let networks go much deeper and more efficient
Transfer Learning with Pretrained CNNs — advanced · Fine-tuning a model pretrained on a large dataset for a new, smaller task
Data Augmentation for Images — advanced · Artificially expanding a training set with transformations to improve generalization
Object Detection Basics (YOLO, Faster R-CNN) — advanced · Locating and classifying multiple objects within a single image
Image Segmentation Basics (U-Net) — advanced · Classifying an image at the pixel level rather than the whole-image level