Machine Learning
Neural networks, CNNs, transformers and training at depth.
What a weight really is, why a single neuron is just linear regression, and how hidden layers and depth let a network learn curves. Interactive, step by step.
Why plain neural networks fail on images, how convolution fixes it, the classic architectures from AlexNet to ResNet, and how YOLO finds objects in real time.
The state of the art in image detection today: YOLO26, RF-DETR, open-vocabulary models and SAM 3, and how they are used from pill counting to self-driving cars.