Berkley Deep Unsupervised Learning

CS294-158 Deep Unsupervised Learning Spring 2019

Lectures, papers and assignments for Berkeyls Deep Unsupervised Learning Course are now available here:

The course deals with 2 areas of deep learning, namely Deep Generative Models and Self-supervised Learning.

Topics are:

  • Generative adversarial networks
  • variational autoencoders
  • autoregressive models
  • flow models
  • energy based models
  • compression
  • self-supervised learning
  • semi-supervised learning.

The course is currently ongoing so not all lectures are available yet.

The final Deep Learning Specialization course is now out

After a long wait, the final and much-anticipated course in the Coursera Deep Learning Specialization series taught by Andrew Ng, called Sequence Models, has now been released.

The first week will be about Recurrent Neural Networks, the second week will address Natural Language Processing & Word Embeddings and the final week will be about Sequence models & Attention mechanism. 2018 has been released, and it’s truly awesome

Last year the 2017 course of was amazing, which taught state of the art deep learning to coders. There are so many goodies in the blog post about the 2018 launch which is available now. This year they held the course using Pytorch instead of Keras and wrote their own library for speeding up development and were the first to add several implementations from papers to the library such as Learning Rate Finder (Smith 2015) and Stochastic Gradient Descent with Restarts (SGDR). With one line of code, you can also get the images that the classifier gets wrong.

17 of the 20 top participants in a kaggle competitors were students in the preview course.

I recommend reading the blog post and taking the course.

There is a new kid on the block in terms of online courses on Deep Learning. is a set of Jupyter notebooks that teach you the basics and different concepts you need in order to get started and being productive in Depp Learning. They are also videos supporting the notebook, although not for every notebook yet.

It differs from in that the videos are shorter and the notebooks are mostly self-explanatory.

The goal of the project is to make Deep Learning accessible to everyone, make it practical, make learning open source and fun.

These are the topics covered:

  1. Lesson 0: Introduction to regression.
  2. Lesson 1: Penalising weights to fit better (scikit learn intro)

Mathematics (optional)

  1. Lesson 2: Gradient Descent. Using basic optimisation methods.
  2. Lesson 3: Tensorflow intro: zero layer hidden networks (i.e. normal regression).
  3. Lesson 4: Tensorflow hidden layer introduction.

Deep Learning

  1. Lesson 5: Using Keras to simplify multi-layer neural nets.
  2. Lesson 6: Embeddings to deal with categorical data. (Keras)
  3. Lesson 7: Word2Vec. Embeddings to visualise words. (Tensorflow)
  4. Lesson 8: Application – Bike Sharing predictions
  5. Lesson 9: Choosing Number of Layers and more
  6. Lesson 10: XGBoost – A quick detour from Deep Learning
  7. Lesson 11: Convolutional Neural Nets (MNIST dataset)
  8. Lesson 12: CNNs and BatchNormalisation (CIFAR10 dataset)
  9. Lesson 13: Transfer Learning (Dogs vs Cats dataset)

Advanced Topics

  1. Lesson 14: LSTMs – Sentiment analysis.
  2. Lesson 15: LSTMs – Shakespeare.
  3. Lesson 16: LSTMs – Trump Tweets.
  4. Lesson 17: Trump – Stacking and Stateful LSTMs.
  5. Lesson 18: Fake News Classifier

You can read more here.

Course 4 [] has been released!

The fourth course, Convolutional Neural Networks of has now been released on coursera. People have been waiting for this one, but i think that the delay was to make the material very up to date with current research results. The four weeks of learning deals with:

  1. Foundations of Convolutional Neural Networks
  2. Deep convolutional models: case studies
  3. Object detection
  4. Special applications: Face recognition & Neural style transfer