Deep Learning Course (NYU, Spring 2020) Yann Lecun
共32课时 1天18小时43分17秒秒
简介
Deep Learning course at NYU, Spring 2020. Taught by Yann LeCun & Alfredo Canziani. With practical applications using PyTorch.
图灵奖获得者给你讲深度学习
Yann LeCun,CNN之父,纽约大学终身教授,与Geoffrey Hinton、Yoshua Bengio并成为“深度学习三巨头”。前Facebook人工智能研究院负责人,IJCV、PAMI和IEEE Trans 的审稿人,他创建了ICLR(International Conference on Learning Representations)会议并且跟Yoshua Bengio共同担任主席。
1983年在巴黎ESIEE获得电子工程学位,1987年在 Université P&M Curie 获得计算机科学博士学位。1998年开发了LeNet5,并制作了被Hinton称为“机器学习界的果蝇”的经典数据集MNIST。2014年获得了IEEE神经网络领军人物奖,2019荣获图灵奖。
1983年在巴黎ESIEE获得电子工程学位,1987年在 Université P&M Curie 获得计算机科学博士学位。1998年开发了LeNet5,并制作了被Hinton称为“机器学习界的果蝇”的经典数据集MNIST。2014年获得了IEEE神经网络领军人物奖,2019荣获图灵奖。
章节
- 课时1:Week 1 – Lecture- History, motivation, and evolution of Deep Learning (1小时38分56秒)
- 课时2:Week 1 – Practicum- Classification, linear algebra, and visualisation (52分30秒)
- 课时3:Week 2 – Lecture- Stochastic gradient descent and backpropagation (1小时43分16秒)
- 课时4:Week 2 – Practicum- Training a neural network (56分59秒)
- 课时5:Week 3 – Lecture- Convolutional neural networks (1小时38分15秒)
- 课时6:Week 3 – Practicum- Natural signals properties and CNNs (48分21秒)
- 课时7:Week 4 – Practicum- Listening to convolutions (51分1秒)
- 课时8:Week 5 – Lecture- Optimisation (1小时29分5秒)
- 课时9:Week 5 – Practicum- 1D multi-channel convolution and autograd (44分58秒)
- 课时10:Week 6 – Lecture- CNN applications, RNN, and attention (1小时28分47秒)
- 课时11:Week 6 – Practicum- RNN and LSTM architectures (53分33秒)
- 课时12:Week 7 – Practicum- Under- and over-complete autoencoders (55分3秒)
- 课时13:Week 7 – Lecture- Energy based models and self-supervised learning (1小时37分18秒)
- 课时14:Week 8 – Lecture- Contrastive methods and regularised latent variable models (1小时39分25秒)
- 课时15:Week 8 – Practicum- Variational autoencoders (58分4秒)
- 课时16:Week 9 – Lecture- Group sparsity, world model, and generative adversarial networks (GANs) (1小时58分24秒)
- 课时17:Week 9 – Practicum- (Energy-based) Generative adversarial networks (1小时15分11秒)
- 课时18:Week 10 – Practicum- The Truck Backer-Upper (1小时25秒)
- 课时19:Week 10 – Lecture- Self-supervised learning (SSL) in computer vision (CV) (2小时41秒)
- 课时20:Week 11 – Practicum- Prediction and Policy learning Under Uncertainty (PPUU) (1小时23分18秒)
- 课时21:Week 11 – Lecture- PyTorch activation and loss functions (1小时53分44秒)
- 课时22:Week 12 – Practicum- Attention and the Transformer (1小时18分1秒)
- 课时23:Week 12 – Lecture- Deep Learning for Natural Language Processing (NLP) (1小时40分56秒)
- 课时24:Week 13 – Practicum- Graph Convolutional Neural Networks (GCN) (1小时10分1秒)
- 课时25:Week 13 – Lecture- Graph Convolutional Networks (GCNs) (2小时22秒)
- 课时26:Week 14 – Lecture- Structured prediction with energy based models (2小时7分30秒)
- 课时27:Week 14 – Practicum- Overfitting and regularization, and Bayesian neural nets (1小时11分27秒)
- 课时28:Week 15 – Practicum part A- Inference for latent variable energy based models (EBMs) (59分4秒)
- 课时29:Week 15 – Practicum part B- Training latent variable energy based models (EBMs) (58分56秒)
- 课时30:Matrix multiplication, signals, and convolutions (47分1秒)
- 课时31:Supervised and self-supervised transfer learning (with PyTorch Lightning) (1小时11分23秒)
- 课时32:Four decades in Machine Learning- a Personal Journey by Yann LeCun (1小时31分22秒)
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