Deep Learning

This section focuses on the foundations of deep learning and the first major family of deep architectures: convolutional neural networks. We begin with the perceptron and logistic-style neurons, move through backpropagation and optimization, then build up the main CNN ideas that made deep learning practical for computer vision.

By the end, you'll be able to:

  • Explain how a single neuron extends logistic regression into a neural-network view.
  • Describe forward propagation, backpropagation, activation functions, and optimization choices.
  • Understand why CNNs work well on images and how classic architectures such as LeNet and AlexNet are organized.
  • History and motivation of deep learning
  • Perceptron, sigmoid, cost functions, and gradient descent
  • Shallow vs deep networks, vectorization, and backpropagation
  • Activation functions, initialization, bias-variance, and optimization
  • Regularization, dropout, batch normalization, and softmax
  • CNN building blocks: filters, stride, padding, channels, and pooling
  • Classic CNN architectures: LeNet and AlexNet
Introduction & History