CS231n
A classic Stanford University course on deep learning and computer vision. Its assignments require implementing neural network components from scratch in NumPy, emphasizing deep understanding of underlying principles such as backpropagation.
Core Facts
Timeline (last 90 days)
The Stanford CS231n course has long been regarded as a classic introductory deep learning course, and its Assignments 1 through 2 require students to implement linear classifiers, two-layer neural networks, batch normalization, and convolution layers in NumPy without using any framework at all.
In the CS231n assignments, students get stuck when deriving the softmax cross-entropy gradient because it involves the coupling of the Jacobian with probability normalization; implementing the batchnorm backward pass tests an even finer grasp of the chain rule.
与CMU 11-785类似的高质量公开课包括斯坦福CS231n（计算机视觉）、CS224n（NLP）以及MIT的6.S191
与11-785类似的高质量公开课包括斯坦福的CS231n、CS224n以及MIT的6.S191
All Facts (5)
Andrej Karpathy taught the deep learning course CS231n at Stanford University
70%UnverifiedThe Stanford CS231n course has long been regarded as a classic introductory deep learning course, and its Assignments 1 through 2 require students to implement linear classifiers, two-layer neural networks, batch normalization, and convolution layers in NumPy without using any framework at all.
50%UnverifiedIn the CS231n assignments, students get stuck when deriving the softmax cross-entropy gradient because it involves the coupling of the Jacobian with probability normalization; implementing the batchnorm backward pass tests an even finer grasp of the chain rule.
50%Unverified与CMU 11-785类似的高质量公开课包括斯坦福CS231n（计算机视觉）、CS224n（NLP）以及MIT的6.S191
50%Unverified与11-785类似的高质量公开课包括斯坦福的CS231n、CS224n以及MIT的6.S191
50%