慕尼黑大学：深度学习介绍 （本课程为高级深度学习方法的先修课）

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https://dvl.in.tum.de/teaching/i2dl-ss19/

课程目录

  • 25.04 - THURSDAY - Lecture 1: Introduction to the lecture, Deep Learning, Machine Learning.
  • 29.04 - Lecture 2: Machine Learning Basics: Linear regression, Classification and Loss Functions.
  • 06.05 - Lecture 3: Introduction to neural networks
  • 13.05 - Lecture 4: Backpropagation
  • 20.05 - Lecture 5: Optimization I
  • 27.05 - Lecture 6: Optimization II
  • 03.06 - Lecture 7: Training Neural Networks Part I: Regularization, Activation functions, Weight initialization, Gradient flow, Batch normalization and Hyperparameter optimization.
  • 10.06 - No lecture - Holidays
  • 17.06 - No lecture - CVPR
  • 24.06 - Lecture 8: Training Neural Networks Part II: Parameter updates, Ensembles and Dropout.
  • 01.07 - Lecture 9: Convolutional Neural Networks (CNN) I
  • 08.07 - Lecture 10: CNN II: common architectures, VGG, ResNet, Inception
  • 15.07 - Lecture 11: Recurrent networks (RNN), LSTM
  • 22.07 - Lecture 12: Guest lecture

教师团队

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练习

EXERCISE 0:

  • Topics: Setup and test the submission system

EXERCISE 1:

  • Topics: Softmax classifier & Two-layer NN
  • Starting date: 20.11.2018
  • Due date: 3.12.2018, 23:59

EXERCISE 2:

  • Topics: Fully Connected Neural Network
  • Starting date: 4.12.2018
  • Due date: 17.12.2018, 23:59

EXERCISE 3:

  • Topics: Classification with PyTorch and Image Segmentation
  • Starting date: 8.1.2019
  • Due date: 22.1.2019, 17:59

EXERCISE 4:

  • Topics: Facial Keypoint detection and RNN MNIST classification
  • Starting date: 22.1.2019
  • Due date: 4.2.2019, 23:59

课件一览
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