- 1.Week_1_–_Lecture__History_motivation_and_evolution_of_Deep_Learning
- 2.Week_1_–_Practicum__Classification_linear_algebra_and_visualisation
- 3.Week_2_–_Lecture__Stochastic_gradient_descent_and_backpropagation
- 4.Week_2_–_Practicum__Training_a_neural_network
- 5.Week_3_–_Lecture__Convolutional_neural_networks
- 6.Week_3_–_Practicum__Natural_signals_properties_and_CNNs
- 7.Week_4_–_Practicum_Listening_to_convolutions
- 8.Week_5_–_Lecture_Optimisation
- 9.Week_5_–_Practicum__1D_multichannel_convolution_and_autograd
- 10.Week_6_–_Lecture_CNN_applications_RNN_and_attention
- 11.Week_6_–_Practicum_RNN_and_LSTM_architectures
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。
Contribution instructions
1. Week11.1. Motivation of Deep Learning, and Its History and Inspiration
1.2. Evolution and Uses of CNNs and Why Deep Learning?
1.3. Problem Motivation, Linear Algebra, and Visualization
2. Week22.1. Introduction to Gradient Descent and Backpropagation Algorithm
2.2. Computing gradients for NN modules and Practical tricks for Back Propagation
2.3. Artificial neural networks(ANNs)
3. Week33.1. Visualization of neural networks parameter transformation and fundamental concepts of convolution
3.2. ConvNet Evolutions, Architectures, Implementation Details and Advantages.
3.3. Properties of natural signals
4. Week 44.1. Linear Algebra and Convolutions
5. Week55.1. Optimization TechniquesI
5.2. Optimization Techniques ll
5.3. Understanding convolutions and automatic differentiation engine
6. Week66.1. Applications of Convolutional Network
6.2. RNNs, GRUs, LSTMs, Attention, Seq2Seq, and Memory Networks
6.3. Architecture of RNN and LSTM Model
7. week77.1. Energy-Based Models
7.2. SSL, EBM with details and examples
7.3. Introduction to Autoencoders
