- 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)
推荐一门由深度学习泰斗，Yann LeCun主讲的深度学习基础课程，纽约大学2020深度学习新课《深度学习(pytorch)》。
本课程涉及深度学习和表示学习的最新技术，重点是有监督和无监督的深度学习、嵌入方法、度量学习、卷积网和递归网，并应用于计算机视觉、自然语言理解和语音识别。期望学生最好有一定的数据科学和机器学习基础知识。
本课程涉及深度学习和表示学习的最新技术，重点是有监督和无监督的深度学习、嵌入方法、度量学习、卷积网和递归网，并应用于计算机视觉、自然语言理解和语音识别。
● Course public folder: bit.ly/DLSP20.
● Class material available
● Piazza Q&A interface available here. Sign-up token: DLSP20.
