There is a new kid on the block in terms of online courses on Deep Learning.
DeepSchool.io 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 fast.ai 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:
Lesson 0: Introduction to regression.
Lesson 1: Penalising weights to fit better (scikit learn intro)
Lesson 2: Gradient Descent. Using basic optimisation methods.
Lesson 3: Tensorflow intro: zero layer hidden networks (i.e. normal regression).
Lesson 4: Tensorflow hidden layer introduction.
Lesson 5: Using Keras to simplify multi-layer neural nets.
Lesson 6: Embeddings to deal with categorical data. (Keras)
Lesson 7: Word2Vec. Embeddings to visualise words. (Tensorflow)
Lesson 8: Application – Bike Sharing predictions
Lesson 9: Choosing Number of Layers and more
Lesson 10: XGBoost – A quick detour from Deep Learning
You heard of DeepMinds AlphaGo that beat worlds best Go player in the game everyone said computers would still need ten years to beat humans in.
That version trained on millions of expert human gameplays and then trained on itself through reinforcement learning.
This version skips all human gameplay and learns by playing against itself through a novel reinforcement learning method. It only has the rules of the game and starts to play against itself, making adjustments and keeping the versions that improve.
If you would like to replicate the research, there is an open source project that is based on the paper https://github.com/gcp/leela-zero. However, in order to get the same results as AplhaGo Zero, you would need to have the same weights, and in order to achieve similar weights, you would need to have access to the same computing power as they. It would take 1700 years on commodity computers. The projects aim is to make a distributed effort to repeat the work.
The fourth course, Convolutional Neural Networks of Deeplearning.ai 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:
Foundations of Convolutional Neural Networks
Deep convolutional models: case studies
Special applications: Face recognition & Neural style transfer
The “YOLO9000: Better, Faster, Stronger” paper describes the improvements to the YOLO, You only look once, architecture that enables realtime object detection and classification. It can classify over 9000 object categories and outperforms Faster RCNN with ResNet and SSD while being significantly faster. They train on both COCO dataset for detection simultaneously with ImageNet for Classification and combine it with a wordtree so that they can also fallback to “dog” if they cannot classify for instance a specific dog breed.