Machine learning projects are often harder than they should be. We’re just running software, and the result is a trained ML model. But three months later do you remember how to rerun the software, the datasets may have changed, and therefore you might be unable to replicate the results. A lack of software tools to manage machine learning datasets is the culprit, and impede efforts to efficiently share of data with colleagues.

In our search for tools to efficiently manage machine learning projects these principles are important:

• Transparency: Inspecting every part of the ML project
• Audibility: Inspecting all intermediate results, and the final result
• Reproducibility: Ability to robustly rerun the software and associated datasets from any stage in the project
• Scalability: Ability to support ML projects containing any number of people, and to work on multiple projects at a time

The article explains implementation in ML projects and using some open source tools like MLFlow and DVC in this context: Principled Machine Learning – DEV Community

Could someone please tell some idea of final project in machine learning? For example how to apply machine learning in healthcare, finance, etc.

I’m currently 9 months into my MASc with my thesis focusing on computer vision and human movement analysis with deep learning. Right now I’d say that I’m at an intermediate level in terms of my ML knowledge. I’ve completed two university courses so far on ML/DL, Andrew Ng’s course, as well as a couple other online courses and readings. I also have taken a pattern classification course that gave me a pretty good background on statistics and its relations with ML from linear regression to HMMs.

I was wondering if anyone knows of any good resources that I can turn to now. Specifically in the area of computer vision or DL would be useful. I find that many websites, like towardsdatascience, end up being the same basics that I’ve seen many times. I’m open to any types of resources really: textbooks, papers, youtube videos, etc.

Also, I was wondering if anyone has experience with auditing classes (just sitting in and listening) during their MASc or PhD. Is it worthwhile?

https://minimaxir.com/apps/gpt2-reddit/

This is my web UI for a finetuned GPT-2 model on a very large amount of Reddit submissions, but with a twist: you can specify the subreddit you want to generate from, and keywords/keyphrases to condition the text upon. For example, here are examples of /r/legaladvice titles conditioned on cat, dog, sue, and tree, and the model typically does a good job of incorporating all the inputs!

Some other good subreddits for generating text are /r/amitheasshole, /r/confession, /r/writingprompts, /r/relationships, and of course the default /r/askreddit .

Technical notes on this Reddit model/API:

• The model is running on Google Cloud Run (via gpt-2-cloud-run), which means it’s slower than GPU-backed GPT-2 demos, but it’s very cheap and can scale up to Reddit-level traffic without any engineering effort. (and it can generate texts in parallel if you want to try many possibilities)
• Unlike /r/SubSimulatorGPT2, which has a separate GPT-2 345M model for each subreddit, this model uses a single GPT-2 (117M) model. This has its advantages: the model is able to incorporate syntax/keywords from other subreddits for more creative output.
• The methodology I use to allow GPT-2 to incorporate arbitrary keywords/keyphrases in generation will be released at some point, but it’s not ready yet.
• The subreddits used in the training set consist of every major subreddit you’ve heard of. The super niche subreddits may not be present, but the network does a good job at extrapolating subreddit type if there is a similar name in the input dataset. (here is the full list of subreddits in the training set; 5000 total)
• The temperature is hardcoded at 0.7 and the top_k at 40 because the results become very weird otherwise (see the fanfiction output, which was done at temperature=1.0 and top_p=0.9)
• Subreddits known for their informative titles work better than image-oriented subreddits, unsurprisingly.
• Not all generated output will be good/make sense, as is the case with any other type of text generation. Please don’t comment “wow the text generation sucks!”, it always takes a few tries. (but like the original GPT-2 model, the signal-to-noise ratio is better than RNN/Markov approaches)
• If you do huge mismatches of the keywords/prompt and the subreddit, the AI might ignore it.

I’m also thinking about creating another SubredditSimulator-type subreddit with generations from all subreddits but on a specific keyword/phrase.

I hope you have fun with it! Let me know if you make any interesting generations!

https://medium.com/ai%C2%B3-theory-practice-business/teach-game-playing-agents-to-play-video-games-solely-from-audio-cues-50120a0db1b2?postPublishedType=repub

Game-playing AI research has focused for a long time on learning to play video games from visual input or symbolic information. However, humans benefit from a wider array of sensors which we utilise in order to navigate the world around us. In particular, sounds and music are key to how many of us perceive the world and influence the decisions we make. In this paper, we present initial experiments on game-playing agents learning to play video games solely from audio cues. We expand the Video Game Description Language to allow for audio specification, and the General Video Game AI framework to provide new audio games and an API for learning agents to make use of audio observations. We analyse the games and the audio game design process, include initial results with simple Q~Learning agents, and encourage further research in this area.

Can someone suggest any project ideas for undergrad? Any suggestions on how Machine learning could be applied in building a real world application to put it to use? A newbie here, trying to learn.

I’m having trouble wrapping my head around how optimisers like ADAM and Momentum differ from second-order optimization methods.

The latter involves calculating/approximating the Hessian however the momentum based optimisers adjust their gradients from past steps (which is quite similar to how higher order derivatives work).

I know that mathematically and implementation-wise these two methods are different however can anyone provide any intuition as to how they differ in practice – perhaps by giving an example of where you would expect wildly different results from these two types of optimisers.

Thanks 🙂

Hey folks – long time lurker here. I’ve been following Strubell et al’s paper which was some pretty decent research showing that complex neural models like BERT and neural architecture search approaches are pretty energy intensive and the mainstream media reaction which seems to have been along the lines of “ALL AI MODELS EVAR GIVE OFF MORE CO2 THAN A CAR. BAN AI RESEARCH!”

I wrote a blog post proposing some steps that data scientists in industry could take to reduce carbon footprint and also as a barometer of interest in either a) making the reporting of energy consumption standard at ML conferences or b) a conference venue for “energy efficient” ML model submissions.

inb4 I get downvoted to hell for self-promotion of my blog post.

Hello,

Here is Olivia, your new personal assistant and best friend.
It is completely written in Golang with a Neural Network.
Enough talking, here is a video showing can Olivia can do https://www.youtube.com/watch?v=JmJZi9gmKvI

https://github.com/olivia-ai/olivia