Here is a list of important Deep Learning related papers. Some fundamental Papers can be found here.
Create your own server, AWS p2 instances or EC2 Spot instances
Regardless of your approach, running deep learning requires resources. One of the reason of it’s current success is that the two last parts of the three key components have emerged.
The three key components are universal algorithm, data and compute.
Compute is available, but at a cost.
There are three possibilities i am considering.
– buy or build your own GPU powered server, for about $1700.
– continue on AWS p2 for about a dollar an hour, supported with an old desktop computer with a decent computer (it is slightly slower than the p2, and runs out of memory occasionally)
– Follow this tutorial and get an AWS p2 as an EC2 Spot instance, where you bid for “unused” resources. According to the author of this post, the AWS bills are a tent of the one for the normal AWS p2.
The most fun alternative is to build your own machine, make it upgradable and don’t have to worry about forgetting to shut down the running cloud instance.
Course: Parallell programming with the GPU
This free course by Udacity and NVIDIA teaches you how to get your mind around and do parallell programming with the GPU. You will use the CUDA programming environment (that you also use in deep learning) in order to use the GPU for your processing. You will have access to high-end GPU machines. Things you will learn centers around image processing.
Nvidia Deep Learning Institute
It is an awesome age we live in where the knowledge you need for tomorrow is available for free for everyone (with a computer, and an internet connection). There is more to learn than there is time to learn it in. We all can become experts in our fields. You must, however find places and situations to put your knowledge into practice so that it will not wane away. I think it is awesome that Nvidia has a learning institute with free courses to help you learn cutting edge stuff. By learning from a company focused on the advancing of the field, and who actually has only to gain from us learning us more, will keep you on the frontiers of the field.
Fast AI course on Computational Linear Algebra
You need to put in 20% of your learning time into math in order to get great at machine learning. Linear algebra and statistics are two very imprtant topics to cover. Here is a fast.ai course on Computational Linear Algebra taught in a different way. It is very hands on. You will be programming.
AI wins agains the best professional dota players
OpenAi developed an AI that wins agains the best professional dota 2 players in the world in 1-on-1 games. It does not use imitation-learning or tree search to learn. Instead it learns by playing agains a copy of itself continuously improving. The game is very complicated and if you would code the ai by hand you would maybe create a quite poor player. By having the computer to teach itself to play it learns a lot of tactics.
read more at:
https://blog.openai.com/dota-2/
Here are tactics it learned by itself:
Demystifying Tensorflow
https://github.com/vahidk/EffectiveTensorflow Attempts to demystify Tensorflow and provide some guidelines and best practices for more effective use of Tensorflow.
An explanation of Recurrent Neural Networks and why they are so magical
Two years ago Karpathy wrote a great article about Recurrent Nerual Networks.
Here is the link, enjoy: The Unreasonable Effectiveness of Recurrent Neural Networks
covfefe.
DeepMind and Blizzard releases Starcraft II as an AI research environment
AIs learning to play atari games are very impressive, beating Go champions was an eye opener to the world. Now DeepMind together with Blizzard releases Starcraft II as an ai research environment
It will be very interesting to see what happens and to try it out.
I have attempted at creating AI scripts for Age of Empires II (which is the best game ever btw) and there are quite good scripts for it. It is however limited by the API that the scripting engine in AOE2 has, and there the scripts are just looped over and over again and if a condition is met, that particular rule is executed.
In this case, you will get a half a million anonymized game replays, a machine learning API, a connection between DeepMinds toolset and Blizzards API.
It will be very interesting to see how deep learning can take on this.
I can imagine we will se pro-like reactions to be used agains user tactics. When you are scripting an ai for instance for AOE2, you need to take a whole bunch of tactics into account. And once you know how an ai script behaves you can easily beat it. Even thought the “new” ai script made for the newest releases for AOE2HD are considered very difficuly, you can beat it by tower rushing it, making it impossible for the computer to gain an economy advantage since the towers keep them form gathering resources. The benefit of the AI is often that it can multitask.
I can imagine that deep with reinforcement learning the computer will generate tactics to counter pro gamers. I quess however, that it will take a year or two before we see deep learning beat pro-gamers.
I hope to see some very interesting games…
On the other hand. I am not sure that i think it is that very good to put the efforts of AI research into developing war strategy machine learning.
Here is the paper.
Andrew Ng’s deeplearning.ai has released 5 deep learning specialization courses on Coursera
Andre Ng announced that he has launched five new courses in Deep Learning on Coursera.
The courses range from 2-4 weeks of study per course where you put in 3-6 hours of study per week per course.
- Neural Networks and Deep Learning
- Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Sequence Models
The courses will earn you a certificate and are described as follows:
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.