This tutorial is the follow up of the previous one, which introduced a recent addition to PySC2 known as raw observations and raw actions. Now we can take that knowledge and attempt to teach our bot how to play using reinforcement learning:
submitted by /u/kiarash-irandoust
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I’m an undergraduate data scientist, about to start work on my dissertation project.
I thought I’d create a system that, given someone’s career history and education, predicts what job they’re likely to get, and at what company. Essentially this is to help focus the efforts of job seekers, and help them get to where they belong.
Originally I planned to do this by scraping data from LinkedIn profiles. From the LinkedIn profile, you can obtain information about someone’s current job and employer, as well as their career history and education. Therefore you can see what education and career history (the input) resulted in their current job (the output – the thing I’m trying to predict).
However, with this strategy, I’m running into ethical problems and data protection problems. There’s a good chance my project proposal won’t pass the ethical review. So I’m looking for a new data source without these issues.
I’m pretty new to machine learning, so it’s hard for me to assess what sources of data are viable for this project. Therefore, I was hoping someone more experienced can suggest how I might obtain the data I’m after, without having ethical baggage? Or failing that, at least a hint or pointer would be greatly appreciated.
Thank you
submitted by /u/JakeBSc
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I have 300 images, 100 are of class 1, 100 are of class 2 and 100 are of class 3.
My CNN model can differentiate between class 1 and class 2 & class 1 and class 3 well. However it gets confused between class 2 and class 3.
How can I make it identify these classes better?
submitted by /u/korokage
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Where do you guys rent compute resources for training? What are your primary selection criteria (cost/reliability/bandwidth/ data location), for your particular use case?
Do you also own your own AI/ML gears for consistent workload, in addition to the cloud? I am asking this as I am building an exchange where people can share quality compute resources at-cost or near-cost. Would this be something that you are interested in? If not, what are the main objections?
And finally, if you are living in Melbourne (Australia) and actively learning/working with/competing in AI/ML, I’d like to take you out for Starbucks.
P.S. The project is close to release (on the technical side) so let me know if you want to poke around with the beta version. The compute (nodes with at least 2x 1080Ti & workstation CPU) is on me!
submitted by /u/thoaionline
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Emily Bender argues for why NLP research should not be conducted only on English.
https://thegradient.pub/the-benderrule-on-naming-the-languages-we-study-and-why-it-matters/
submitted by /u/hughbzhang
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User_id, review 1, "We are a family of 4 adults chose this and view and loved this place" 1, "My husband and I, with our 2 teen sons, visit this restaurant at least once..." 2,"My partner and I booked table for a short holiday, their wine menu was awesome" 2,"My wife is a fan of jazz and she's expecting, so visited this place " We have a bunch of reviews, some examples shown above. Are there any techniques that I can use to infer:
User Id 1 => family of 4, 2 sons (13-19) User Id 2 => family of 2, expecting : Can anyone recommend datasets and/or methods to infer the above?
submitted by /u/bluzkluz
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For anyone trying to learn or practice RL, here’s a repo with working PyTorch implementations of 17 RL algorithms including DQN, DQN-HER, Double DQN, REINFORCE, DDPG, DDPG-HER, PPO, SAC, SAC Discrete, A3C, A2C etc..
Let me know what you think!
https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch
submitted by /u/__data_science__
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I’m trying to find papers and code for visual dubbing, where the visual content is adjusted to match the new audio channel. Thanks!
submitted by /u/cbsudux
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This is the project a friend and me did for our first online hackathon. It uses Mask-RCNN to extract easily objects from pictures. Although it was kinda strange it was really cool to work in an online hackathon, and we’ve learned a lot through the way.
What do you guys think?? https://github.com/AlbertSuarez/object-cut
You can actually test it at: https://objectcut.ga/
Give it some love 😀
submitted by /u/adriacabeza
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![[D] Super-Convergence Skepticism](https://b.thumbs.redditmedia.com/HuGLR_1Qv_LaBiXsoYQJwMJkM3_xWB22X0BCoJ3lHGU.jpg "[D] Super-Convergence Skepticism") |
Smith and Topin’s 2017 paper Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates garnered quite a bit of attention, promising to cut training iterations by an order of magnitude without compromising accuracy. They propose the following change to the learning rate scheduler: https://i.redd.it/4luxa8wdamm31.png Using a 56-layer residual network, they claim that while it takes 80k iterations to train to 91% accuracy on CIFAR-10 using conventional algorithms, but that they can achieve a higher accuracy (92.4%) in only 10k iterations. On Open Review there is concern that it’s not clear if the accuracy gains are significant (“no error bars”) and about whether this technique generalizes to other architectures. I think we’ve mostly seen that Super-Convergence seems to converge to fine results on multiple architectures — the “train ImageNet in 3 hours for $25” is probably the most well-known example. I’ve used it to train ResNet-18, 34, and 50 on CIFAR. I’m not sure it actively/consistently improves test accuracy, but heck: even if it decreases accuracy a bit, if it cuts training time by a factor of 8 I’ll happily use it! My main problem with the Super-Convergence paper is that the baseline they compare too seems laughably un-optimized for their problem. Consider the training run below. I grabbed the ResNet model from this github repo. There’s nothing particularly special here. Some minor things worth noting:
https://i.redd.it/glqe36c6rnm31.png Obviously this isn’t an apples-to-apples comparison with the Super-Convergence paper, which trains a 56-layer ResNet. Maybe the larger network actually does just require far longer to train? While the larger network does require more time, it is unlikely to make a huge difference. Consider the two runs I ran with ResNet50. Here I had to drop the batch size to 64 (I’m just using my personal PC). The second run has a batch size of 128 with half precision (Note: I need a brief warm-up period here due to some NaNs). While there is a clear problem with the network not converging quickly enough, the paper uses a batch size of 1000. One might be skeptical (without an actual experiment) that this would let us train in 10k iterations, but training in 20k seems easy. https://i.redd.it/wsa59jidsnm31.png To be clear, I think there are valuable ideas used in this paper. I have nothing against the learning rate test (proposed in an earlier paper). Warm-up periods seem useful as a means of being able to eventually use a higher learning rate, and I like that the LR test and training regimen acknowledge the fact that you can often use a higher learning rate after a bit of warm up (though I do think spending half of your training time warming up is excessive and I’m skeptical that it helps generalization). The observation that you can avoid having your training loss flat-line (and wasting time!) by decreasing the learning rate slowly is useful. If the claim that this corner-cutting doesn’t have a detrimental effect on test accuracy is true (and I think it can be) then this is a very interesting observation! But I’m not convinced at all that Super-Convergence delivers on its promise of bringing training times down by an order of magnitude — not if it is compared against a learning-rate scheduler that has been tuned to any extent. submitted by /u/unabashed-p-zombie |
