10-601 Deep Learning 1

This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016

Slides

• Slides in PowerPoint,Slides in PDF.
• Slides in PowerPoint,Slides in PDF.

Readings

This area is moving very fast and the textbooks are not up-to-date. Some recommended readings:

• Neural Networks and Deep Learning An online book by Michael Nielsen, pitched at an appropriate level for 10-601, which has a bunch of exercises and on-line sample programs in Python.
• Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition has nice on-line notes.

I also used some on-line visualizations in the materials for the lecture, especially the part on ConvNets.

• the Wikipedia page for convolutions has nice animations of 1-D convolutions.
• On-line demo of 2-D convolutions for image processing.
• There's an on-line demo of CNNs which are trained in your browser (!)
• 3D visualization of a trained net.

For more detail, look at the MIT Press book (in preparation) from Bengio - it's very complete but also fairly technical.

Things to remember

• The underlying reasons deep networks are hard to train
  • Exploding/vanishing gradients
  • Saturation
• The importance of key recent advances in neural networks:
  • Matrix operations and GPU training
  • ReLU, cross-entropy, softmax
• Convolutional networks
  • 2-d convolution
  • How to construct a convolution layer
  • Architecture of CNN: convolution/downsampling pairs