Deep Learning lectures
This post highlights some great lectures about deep learning
Stanford CS229
- Only 1 [slide] which gives an intro about deep learning.
Quoc Le’s Lectures on Deep Learning
- [Original lectures]
- If you are interested in high level overview of deep learning, these lectures will be a great for your needs. The lectures only have 3 videos of 1 hour each.
Update: Dr. Le has posted tutorials on this topic: Part 1 and Part 2.
Dr. Quoc Le from the Google Brain project team (yes, the one that made headlines for creating a cat recognizer) presented a series of lectures at the Machine Learning Summer School (MLSS ’14) in Pittsburgh this week. This is my favorite lecture series from the event till now and I was glad to be able to attend them.
The good news is that the organizers have made available the entire set of video lectures in 4K for you to watch. But since Dr. Le did most of them on the board and did not provide any accompanying slides, I decided to put the contents of the lectures along with the videos here.
In this post I posted Dr. Le’s lecture videos and added content links with short descriptions to help you navigate them better.
Event Type | Description | Course Materials |
---|---|---|
Lecture | Neural Networks Review |
[Video] [deep-learning-tutorial-1] |
Lecture | NNs in Practice | [Video] |
Lecture | Deep NN Architectures |
[Video] [deep-learning-tutorial-2] |
CS231n: Convolutional Neural Networks for Visual Recognition
- This class teaches you how to apply deep learning on Computer Vision. A faily practical class on using Python for machine learning tasks.
- Links:
Syllabus
Event Type | Date | Description | Course Materials |
---|---|---|---|
Lecture | Jan 4 | Intro to Computer Vision, historical context. | [slides] [video] |
Lecture | Jan 6 | Image classification and the data-driven approach k-nearest neighbor Linear classification I |
[slides]
[video]
[python/numpy tutorial] [image classification notes] [linear classification notes] |
Lecture | Jan 11 |
Linear classification II Higher-level representations, image features Optimization, stochastic gradient descent |
[slides]
[video]
[linear classification notes] [optimization notes] |
Lecture | Jan 13 | Backpropagation Introduction to neural networks |
[slides]
[video]
[backprop notes] [Efficient BackProp] (optional) related: [1], [2], [3] (optional) |
Lecture | Jan 18 | Holiday; No class. | |
A1 Due | Jan 20 | Assignment #1 (kNN/SVM/Softmax/2-Layer Net) Due date | [Assignment #1] |
Lecture | Jan 20 | Training Neural Networks Part 1 activation functions, weight initialization, gradient flow, batch normalization babysitting the learning process, hyperparameter optimization |
[slides]
[video]
Neural Nets notes 1 Neural Nets notes 2 Neural Nets notes 3 tips/tricks: [1], [2], [3] (optional) Deep Learning [Nature] (optional) |
Lecture | Jan 25 |
Training Neural Networks Part 2: parameter updates, ensembles, dropout Convolutional Neural Networks: intro |
[slides]
[video]
Neural Nets notes 3 |
Lecture | Jan 27 |
Convolutional Neural Networks: architectures, convolution / pooling layers Case study of ImageNet challenge winning ConvNets |
[slides]
[video]
ConvNet notes |
Proposal due | Jan 30 | Couse Project Proposal due | [proposal description] |
Lecture | Feb 1 |
ConvNets for spatial localization Object detection |
[slides] [video] |
Lecture | Feb 3 |
Understanding and visualizing Convolutional Neural Networks Backprop into image: Visualizations, deep dream, artistic style transfer Adversarial fooling examples |
[slides] [video] |
A2 Due | Feb 5 | Assignment #2 (Neural Nets) Due date | [Assignment #2] |
Lecture | Feb 8 |
Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM) RNN language models Image captioning |
[slides]
[video]
DL book RNN chapter (optional) min-char-rnn, char-rnn, neuraltalk2 |
Midterm | Feb 10 | In-class midterm | |
Lecture | Feb 15 | Holiday; No class. | |
Milestone | Feb 17 | Course Project Milestone | |
Lecture | Feb 17 |
Training ConvNets in practice Data augmentation, transfer learning Distributed training, CPU/GPU bottlenecks Efficient convolutions |
[slides] [video] |
Lecture | Feb 22 | Overview of Caffe/Torch/Theano/TensorFlow | [slides] [video] |
A3 Due | Feb 24 | Assignment #3 (ConvNets) Due date | [Assignment #3] |
Lecture | Feb 24 |
Segmentation Soft attention models Spatial transformer networks |
[slides] [video] |
Lecture | Feb 29 |
ConvNets for videos Unsupervised learning |
[slides] [video] |
Lecture | Mar 2 | Invited Talk: Jeff Dean | [video] |
Lecture | Mar 7 | Student spotlight talks, conclusions | [slides] |
Poster Presentation | Mar 9 | ||
Final Project Due | Mar 13 | Final course project due date | [reports] |