LeCun主讲！纽约大学《深度学习》2021课程全部放出
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· 2021-11-18
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获取有趣、好玩的前沿干货！
文章编辑 机器学习与推荐算法
YouTube视频：https://www.youtube.com/watch?v=mTtDfKgLm54
官方中文版讲义：https://atcold.github.io/pytorch-Deep-Learning/zh/
课件：https://github.com/Atcold/NYU-DLSP21
GitHub：hhttps://atcold.github.io/NYU-DLSP21/
Reddit论坛：https://www.reddit.com/r/NYU_DeepLearning/
Theme 1: Introduction
History and resources 🎥 🖥
Gradient descent and the backpropagation algorithm 🎥 🖥
Neural nets inference 🎥 📓
Modules and architectures 🎥
Neural nets training 🎥 🖥 📓📓
Homework 1: backprop
Theme 2: Parameters sharing
Recurrent and convolutional nets 🎥 🖥 📝
ConvNets in practice 🎥 🖥 📝
Natural signals properties and the convolution 🎥 🖥 📓
Recurrent neural networks, vanilla and gated (LSTM) 🎥 🖥 📓📓
Homework 2: RNN & CNN
Theme 3: Energy based models, foundations
Energy based models (I) 🎥 🖥
Inference for LV-EBMs 🎥 🖥
What are EBMs good for? 🎥
Energy based models (II) 🎥 🖥 📝
Training LV-EBMs 🎥 🖥
Homework 3: structured prediction
Theme 4: Energy based models, advanced
Energy based models (III) 🎥 🖥
Unsup learning and autoencoders 🎥 🖥
Energy based models (VI) 🎥 🖥
From LV-EBM to target prop to (any) autoencoder 🎥 🖥
Energy based models (V) 🎥 🖥
AEs with PyTorch and GANs 🎥 🖥 📓📓
Theme 5: Associative memories
Energy based models (V) 🎥 🖥
Attention & transformer 🎥 🖥 📓
Theme 6: Graphs
Graph transformer nets [A][B] 🎥 🖥
Graph convolutional nets (I) [from last year] 🎥 🖥
Graph convolutional nets (II) 🎥 🖥 📓
Theme 7: Control
Planning and control 🎥 🖥
The Truck Backer-Upper 🎥 🖥 📓
Prediction and Planning Under Uncertainty 🎥 🖥
Theme 8: Optimisation
Optimisation (I) [from last year] 🎥 🖥
Optimisation (II) 🎥 🖥 📝
Miscellaneous
SSL for vision [A][B] 🎥 🖥
Low resource machine translation [A][B] 🎥 🖥
Lagrangian backprop, final project, and Q&A 🎥 🖥 📝
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