cs231n.stanford.edu

During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

2017 Video

Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition

Lecture 1 gives an introduction to the field of computer vision, discussing its history and key challenges. We emphasize that computer vision encompasses a wide variety of different tasks, and that despite the recent successes of deep learning we are still a long way from realizing the goal of human-level visual intelligence.

Lecture 2 | Image Classification

Lecture 2 formalizes the problem of image classification. We discuss the inherent difficulties of image classification, and introduce data-driven approaches. We discuss two simple data-driven image classification algorithms: K-Nearest Neighbors and Linear Classifiers, and introduce the concepts of hyperparameters and cross-validation.

Lecture 3 | Loss Functions and Optimization

Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss function to quantify our unhappiness with a model’s predictions, and discuss two commonly used loss functions for image classification: the multiclass SVM loss and the multinomial logistic regression loss. We introduce the idea of regularization as a mechanism to fight overfitting, with weight decay as a concrete example. We introduce the idea of optimization and the stochastic gradient descent algorithm. We also briefly discuss the use of feature representations in computer vision.

Lecture 4 | Introduction to Neural Networks

In Lecture 4 we progress from linear classifiers to fully-connected neural networks. We introduce the backpropagation algorithm for computing gradients and briefly discuss connections between artificial neural networks and biological neural networks.

Lecture 5 | Convolutional Neural Networks

In Lecture 5 we move from fully-connected neural networks to convolutional neural networks. We discuss some of the key historical milestones in the development of convolutional networks, including the perceptron, the neocognitron, LeNet, and AlexNet. We introduce convolution, pooling, and fully-connected layers which form the basis for modern convolutional networks.

Lecture 6 | Training Neural Networks I

Lecture 7 continues our discussion of practical issues for training neural networks. We discuss different update rules commonly used to optimize neural networks during training, as well as different strategies for regularizing large neural networks including dropout. We also discuss transfer learning and finetuning.

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Lecture 13 | Generative Models

In Lecture 13 we move beyond supervised learning, and discuss generative modeling as a form of unsupervised learning. We cover the autoregressive PixelRNN and PixelCNN models, traditional and variational autoencoders (VAEs), and generative adversarial networks (GANs).