- Week 1 – Lecture - History motivation and evolution of Deep Learning
- Week 1 – Practicum - Classification linear algebra and visualisation
- Week 2 – Lecture - Stochastic gradient descent and backpropagation
- Week 2 – Practicum - Training a neural network
- Week 3 – Lecture - Convolutional neural networks
- Week 3 – Practicum - Natural signals properties and CNNs
- Week 4 – Practicum- Listening to convolutions
- Week 5 – Lecture- Optimisation
- Week 5 – Practicum - 1D multi-channel convolution and autograd
- Week 6 – Lecture- CNN applications RNN and attention
- Week 6 – Practicum- RNN and LSTM architectures
- Week 7 – Practicum- Under- and over-complete autoencoders
- Week 7 – Lecture- Energy based models and self-supervised learning
- Week 8 – Lecture- Contrastive methods and regularised latent variable models
- Week 8 – Practicum- Variational autoencoders
- Week 9 – Lecture- Group sparsity world model and generative adversarial networ
- Week 9 – Practicum- (Energy-based) Generative adversarial networks
- Week 10 – Lecture- Self-supervised learning (SSL) in computer vision (CV)
- Week 10 – Practicum- The Truck Backer-Upper
- Week 11 – Lecture- PyTorch activation and loss functions
- Week 11 – Practicum- Prediction and Policy learning Under Uncertainty (PPUU)
推薦一門由深度學習泰斗，Yann LeCun主講的深度學習基礎(chǔ)課程，紐約大學2020深度學習新課《深度學習(pytorch)》。
本課程涉及深度學習和表示學習的最新技術(shù)，重點是有監(jiān)督和無監(jiān)督的深度學習、嵌入方法、度量學習、卷積網(wǎng)和遞歸網(wǎng)，并應用于計算機視覺、自然語言理解和語音識別。期望學生最好有一定的數(shù)據(jù)科學和機器學習基礎(chǔ)知識。
本課程涉及深度學習和表示學習的最新技術(shù)，重點是有監(jiān)督和無監(jiān)督的深度學習、嵌入方法、度量學習、卷積網(wǎng)和遞歸網(wǎng)，并應用于計算機視覺、自然語言理解和語音識別。
● Course public folder: bit.ly/DLSP20.
● Class material available
● Piazza Q&A interface available here. Sign-up token: DLSP20.
