I was planning to learn handcrafting features by referring kaggle notebooks and some other resources .

We are really lucky that all the best players in AI (google, openAI, fb, etc) are all contributing their insights to the public, aren’t we? Even if some may not be open source, they all share their methods in detail.

Could you imagine a world where all the best players hid their techniques? I guess we would be years behind by now.

I am very interested in what is happening at the crossroads between computer science and art, especially algorithmic and ML generated / supported art (music, film, paintings, installations, etc.).

By this, I don’t mean using ML to develop tools that are used in the creative process (improving a 3D modeling software using ML for instance), but using Machine Learning as the creative tool in itself (a standard example being NextRembrandt).

My background is in ML and CS and I have been toying with the idea of pursuing a job / PhD in a related project.

Can anyone point me to any research labs / institutions / projects that they find interesting, fit the description and could maybe accept applications?

I know it is a pretty broad question. I’m looking for opinions from those of you who already know an interesting project that maybe hasn’t had much public attention yet.

Any help would be great! Thank you.

(for those of you without suggestions, but interested in the topic, I leave you this project I found the other day).

Asking for a friend.

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In terms of Ghz/Gb/time/etc.

Interesting paper published in Nature Neuroscience discusses ways where the neuroscience community might benefit from thinking like deep learning people.

Abstract

Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.

Paper: https://www.nature.com/articles/s41593-019-0520-2 (anyone have link to full pdf)?

Summary by the author: https://threadreaderapp.com/thread/1188868863850500096.html

I think my original post was removed because I didn’t tag it.

I have a project in mind. I want to build an image classifier with novel classes. For example, lets say I want to classify images of different types of bicycles. Google images is ripe with these images for each type of bike.

I want to publish a blog post about my project, and put my code (including scraper) on github but not upload the image files anywhere. I might put up a (free) endpoint hosting my resulting classifier if it works.

Questions:

1. Are all images on google images fair game for training data or do I have to limit it to images “labelled for reuse”?
2. Do I have to cite the images I use as training data?
3. I’ve read about “fair use”, how does that figure in here?

Thanks, and sorry if this has been covered elsewhere

Recent work from a group at Google Brain.

Abstract

Much of model-based reinforcement learning involves learning a model of an agent’s world, and training an agent to leverage this model to perform a task more efficiently. While these models are demonstrably useful for agents, every naturally occurring model of the world of which we are aware—e.g., a brain—arose as the byproduct of competing evolutionary pressures for survival, not minimization of a supervised forward-predictive loss via gradient descent. That useful models can arise out of the messy and slow optimization process of evolution suggests that forward-predictive modeling can arise as a side-effect of optimization under the right circumstances. Crucially, this optimization process need not explicitly be a forward-predictive loss. In this work, we introduce a modification to traditional reinforcement learning which we call observational dropout, whereby we limit the agents ability to observe the real environment at each timestep. In doing so, we can coerce an agent into learning a world model to fill in the observation gaps during reinforcement learning. We show that the emerged world model, while not explicitly trained to predict the future, can help the agent learn key skills required to perform well in its environment.

web article: https://learningtopredict.github.io

arxiv: https://arxiv.org/abs/1910.13038

I get it; He is a terrible and shitty person for stealing, plagiarizing, and profiting off of it. However, it’s starting to turn into TMZ in this subreddit with the childish, cancel culture with zero, productive actions. I come her to read about cool research and everyone’s neat projects that they would love to share. I like when people have questions about a paper, or are wanting feedback on their projects. Can we just ban his videos / content?

Hi, I been trying to learn a bit of machine learning for a project that I’m working in and at the moment I managed to classify text using SVM with sklearn and spacy having some good results, but i want to not only classify the text with svm, I also want it to be classified based on a list of keywords that I have. For example: If the sentence has the word fast or seconds I would like it to be classified as performance.

I’m really new to machine learning and I would really appreciate any advice.