Shypd.aiThe largest open index of AI toolsstat479-deep-learning-ss19 provides course material for a Deep Learning class at University Wisconsin-Madison. It includes lecture slides, code examples, and a course calendar covering various deep learning topics.
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STAT479: Deep Learning (Spring 2019) offers comprehensive course material from the University Wisconsin-Madison, instructed by Sebastian Raschka. This resource provides lecture slides and code examples covering a wide array of deep learning topics, from the history of neural networks and foundational concepts like logistic regression and multilayer perceptrons, to advanced subjects such as convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks. The material also delves into practical aspects like automatic differentiation with PyTorch, gradient descent, regularization, and optimization algorithms. It's an invaluable resource for students and educators looking to understand and teach deep learning principles.
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