Hello everyone,

I’m really bored so I decided to make a Hearthstone Battlegrounds simulator in order to teach a bot the best strategies to win a game.

For those of you who don’t know, this game is an incomplete information RNG-based turn-based game where 8 players recruit minions to fight each other automatically.

On the recruiting phase there is a limited set of commands a player can do:

– Roll -> costs 1 gold and refreshes the shop

– Buy -> 3g to buy a minion

– Sell -> Sell a played minion for 1g

– Level Tavern Tier -> pay to get better tier minions

– Play -> play a minion on the board

– Buff -> Same as Play but usually sell the buffer minion right after

– Reorder -> Move a minion to a different position on the board

– Hero power -> Use special hero ability (Only for active abilities)

Of course, it’s a bit more complicated than that cause there are synergies, buffs, triples, etc.. But let’s start slowly.

I currently coded the game and 8 players playing each other. Each player has a “Brain” class that is now very simple: Greed to buy / level and roll last.

The brain is an interface which I inject to each of the players so that I can try different variations of Brains playing each other.

I want to try and write an algorithm that will randomly do a set of commands and “remember” it. after the game ends I want to evaluate the sets that yielded the most points (ie. max damage to the opponent and min damage taken) and save the commands tree so that is can be used in the next games.

Maybe something like a hash of the commands set (Roll -> Buy -> MinionBought -> MinionPlayed -> Level-up etc.) and the score.

The hope is that after many iterations the bot will get smarter and better.

If I talk nonsense it may be because I like expertise on the subject but I really would appreciate all the help and guidance I can get. Cause I have the idea but I don’t know where to begin.

I couldn’t find anywhere that aggregates ongoing machine learning competitions across platforms like Kaggle, DrivenData, AIcrowd, etc, so I created one last week: http://mlcontests.com/

It’s pretty basic at the moment, but I’d love to hear suggestions for things you’d like to see. I’m happy to take pull requests too: https://github.com/mlcontests/mlcontests.github.io

Guys, I’ve been breaking my head about this for quite some time now. I work with Computer Vision (with and without Deep Learning techniques). I am trying to build a PC for the same with more focus on Deep Learning techniques(80%). GPU, memory and storage requirements for this PC are covered (2070 super, 16gb 3200 b-die corsair lpg and adata xpg 1tb nvme m2 ssd). I am not able to decide on the CPU. I came across the mkl issue. I understand matrix computations on the CPU are faster on Intel than an AMD processor. I’ve come across blogs which show this difference. I got aware of the benefits of using libopenblas for speeding up stuff and matching the speed for amd and intel. I also came across articles where they mention using debug variables and patched versions of numpy to match the speed.

So, it takes a bit of an effort but, techinically, it is possible to match the matrix related computational speed of an amd processor to that of an intel processor.

• Am I correct in assuming this?
• can we assume things will get better? Like, will Intel ever start supporting AMD processors? Intel can find some other ways to cut mkl support for amd, though. That seems more likely. The only reason I have this doubt is because AMD does have the architecture for supporting mkl.
• should I not worry too much about this?
• I did not, however, find a direct comparison of i7 9700k vs 3700x for matrix computations. I want to know if it is significant.

I have the following summary about the 2 processors. Please feel free to add/correct/rank the key features that I mention below.

AMD:

cheaper lesser power setup cost is less - i need not buy a fan for the processor. also, ryzen's fan looks cooler. more threads - correct me if i am wrong here but this allows for more computations when compared to an i7. 

Intel:

mkl support - easier to setup/no headaches about support loss sli support motherboards are more common compared to x570 chipset motherboards for ryzen 

Thanks in advance!

Hi! I’m a second year PhD student in machine learning and I was looking for an Internship (possibly) in Europe to apply for next year, 4 months ideally.

My lab does mainly theoretical work and started doing a bit of deep learning just 3 years ago. When I started i was more focused on the experiments (deep learning) and now i am shifting towards the theoretical side due to the lab and supervisor expertise. Since i like both aspects of machine learning I though that an internship could be a good opportunity to make more impactful experimental work and also make some connections.

I only have 2 (ICML/Neurips) publication as a co-author. Do you think something like Deepmind in London could already be out of reach for me? Do you know about some nice internships program that could suit me better?

Let’s say we have two (or more) embedding spaces learned from different data spaces:

There is one one global task T that all embedding spaces are evaluated on.

To perform better on T than each embedding space would on their own it follows that we can just concatenate each vector of each embedding space. But is there a better method than to simply concatenate?

Hi, I’m doing some research and playing with the waymo dataset. To start I’m doing simple 2d object detection on their subset of data with 2d bounding boxes. Has anyone done similar? I am having trouble setting an expectation of accuracy.

Furthermore the dataset seems to be chunked into individual drive segments, where a lot of the images are temporally ‘close’ meaning the same cars are in the frame. I believe this is causing early overfitting. Wanted to see if anyone else is experiencing this.

Thanks!

https://teddykoker.com/2019/12/beating-the-odds-machine-learning-for-horse-racing/

For a recent project, I set out to see if I could use machine learning to identify inefficiencies in horse racing wagering. It was interesting to find how such methods can work, even without much in-domain knowledge.