So I’m sure some of you if not a good portion of you have heard about the idea that captchas are used to teach machines, and I don’t know enough about the topic to say if it’s true or not, it may just be a theory or objectively true, I honestly have no idea.

I just had a question about it; if it was true that captchas are used to teach machines, how does that even work? Captchas already have pre-set correct answers right? Doesn’t that mean that machines wouldn’t be learning anything new because the correct area for the object in that captcha has already been defined? Excuse my stupidity if there’s a simple answer to this, but like I said I have no idea about this topic and I’m just curious. Thanks!

Follow up post: (Looking for some more reviews on automl before I start using it)

I’ve heard AutoML(google or amazon) is used to find the optimal ML model.

–>How does AutoML really do when it comes to model selection?

–>what about when it comes to cross-validation of the models?

–>Does it really find the ‘best’ model?

—>What are your criteria of what’s considered best?

“weak/slow;

interpretability/representability;

replicability?

confidence/performance;

biased/data-hungry.”

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Welcome all reviews-good/bad! 🙂

I’ve been stumped on this problem for a week or so.

I realized that with MCTS, if you don’t keep track of which subtrees have been fully saturated (i.e. you’ve expanded it completely, down to every leaf), you can end up revisiting the same paths over and over again, when you really should be using your time exploring other areas of the tree.

However, at the same time, if you naively implement behavior that says “During the selection phase, ignore any fully-saturated nodes”, then the metric “number of visits” is no longer a good indicator of which child node is the best.

E.x., you have two children nodes, one leads to victory 80% of the time, the other leads to victory 40% of the time. But the one that wins 80% of the time only has 200 nodes in its entire subtree, for a maximum of 200 unique visits. The one that wins only 40% has a huge subtree, so its “visits” count becomes very large.

At the end of MCTS, you’ll see the “wins/plays” for the smaller tree will be better, but since in general MCTS will select “visits” over “wins”, you will end up selecting the wrong node.

I’ve been trying to crack this myself for some time as a matter of pride, but I’m deciding to reach out to see if the community already has a good strategy for this.

Some of my ideas include: for each node, also store a count of “all descendants” (i.e. how many nodes are in this node’s subtree), as well as “all leafs” (i.e. how many nodes in this subtree are leafs), plus “win leafs” and “lose leafs”, and then you can do 1 / all_descendants as a factor to smooth out the difference between smaller subtrees and larger ones…

[Project Overview in 3lines]

1. Why are exchange fees fixed? Can’t we dynamically up and down the fees depending on market conditions?
2. Make a dynamic fee policy environments where reinforcement learning agents learn trading policies
3. Provide insight into what the fee policy is optimized for exchanges

[The algorithm of the Trading agent -pytorch based]

1. PPO + MobileNet
2. RAINBOW + MobileNet
3. RAINBOW + Transformer(MultiheadAttention)

[Your Utility] If you want to use reinforcement learning in stock investment, you can use this source and it will be a baseline! (However, in this simulation, ROI was not the goal. So the performance is not guaranteed. 🙁 )

Below is a 2:30 second video link and a medium article link describing the project.

[Youlink] https://youtu.be/kBjv4KmkEHU

[Medium link] https://medium.com

All source code is available in the following repo https://github.com/deconlab

I am not in the academia and industry of deep learning, but I am interested in general artificial intelligence and have some results. But this is usually the research field of the big ones. For me outsiders, in the eyes of others, I am like folk who studies perpetual motion machine or overthrows the theory of relativity.

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I am looking for an opportunity to step into this field. But before that, sorting out the results and publishing them as professional papers would cost me the energy and time I thought was a waste.

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So my question is: How to behave like a deep-learning insider so that others can read these results seriously?

Attach the GitHub address of my project: https://github.com/TobbysGitHub/General-Artificial-Intelligence/blob/master/micro-prediction%20capsule%20system.md

This is a short markdown document in the project that can be read in less than a minute https://github.com/TobbysGitHub/General-Artificial-Intelligence

I have developed some novel techniques in the project, just to mention one is localized and distributed backpropagation.

Just wanted to say that I’m rewriting the GDL book in Pytorch here https://github.com/MLSlayer/Generative-Deep-Learning-Code-in-Pytorch

Any feedback or help would be appreciated!

I would like to start a discussion about solving deep networks without backprop. I have invented a mechanism in 2016 that uses no backprop at all and that can solve any network topology. The algorithm also scales almost lineary so you can double the performance using two machines.

Anyone interested?

This was one of the datasets used by them to learn a compiler-compatible algorithm to optimize for model parallelism in TensorFlow graphs.

github.com/deepmind/deepmind-research/tree/master/regal