Yann LeCun主講！紐約大學(xué)《深度學(xué)習(xí)》2021課程全部放出，附slides與視頻

來(lái)源：專知

本文附課程，建議閱讀5分鐘?
Yann LeCun在紐約大學(xué)數(shù)據(jù)科學(xué)中心（CDS）主講的《深度學(xué)習(xí)》2021年春季課程現(xiàn)已全部在線可看！

該課程自2021年春季開(kāi)始由Yann LeCun與Alfredo Canziani等共同執(zhí)教。

CDS發(fā)布了Yann LeCun的深度學(xué)習(xí)（DS-GA 1008）課程的所有材料，包括帶英文字幕教學(xué)視頻、書(shū)面講義、課件以及帶有PyTorch實(shí)現(xiàn)的可執(zhí)行Jupyter Notebooks。

課程關(guān)注深度學(xué)習(xí)和表示學(xué)習(xí)的最新技術(shù)，重點(diǎn)關(guān)注有監(jiān)督和無(wú)監(jiān)督深度學(xué)習(xí)、嵌入方法、度量學(xué)習(xí)、卷積和循環(huán)網(wǎng)，以及在計(jì)算機(jī)視覺(jué)、自然語(yǔ)言理解和語(yǔ)音識(shí)別方面的應(yīng)用。前提條件包括:DS-GA 1001數(shù)據(jù)科學(xué)入門(mén)或研究生水平的機(jī)器學(xué)習(xí)課程。

地址：

https://cds.nyu.edu/deep-learning/

資源

• YouTube視頻：
  https://www.youtube.com/watch?v=mTtDfKgLm54
• 官方中文版講義：
  https://atcold.github.io/pytorch-Deep-Learning/zh/
• 課件：
  https://github.com/Atcold/NYU-DLSP21
• GitHub：
  hhttps://atcold.github.io/NYU-DLSP21/
• Reddit論壇：
  https://www.reddit.com/r/NYU_DeepLearning/

授課老師：

目錄內(nèi)容：

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

1. Planning and control??????

2. The Truck Backer-Upper?????????

3. 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?????????

深度學(xué)習(xí)概要