NYU-DLSP20 and NYU-DLSP21
These are successive iterations of the same course curriculum, where the 2021 version supersedes the 2020 version with updated content, making them sequential educational resources rather than tools designed to be used together.
About NYU-DLSP20
Atcold/NYU-DLSP20
NYU Deep Learning Spring 2020
Comprehensive Jupyter notebook collection covering foundational and advanced deep learning topics including convolutional networks, recurrent architectures, and optimization techniques. Material integrates PyTorch for hands-on exercises and is structured with accompanying video lectures and multilingual documentation accessible via Binder for browser-based execution without local setup.
About NYU-DLSP21
Atcold/NYU-DLSP21
NYU Deep Learning Spring 2021
Comprehensive course materials covering foundational deep learning topics (backpropagation, gradient descent, CNNs, RNNs) with a novel emphasis on latent variable energy-based models as a core framework. Includes lecture slides, Jupyter notebooks, and video transcriptions organized into three modules, each with accompanying practicum assignments. Built progressively to enable advanced applications in the second half of the semester by establishing EBMs as a foundational concept rather than an advanced topic.
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