Flux is an elegant approach to machine learning. It's a 100% pure-Julia stack, and provides lightweight abstractions on top of Julia's native GPU and AD support. Flux makes the easy things easy while remaining fully hackable.

#julia, #machine, #learning, #ml, #datascience, #neuralnetworks

🐼 Pandas on Steroids: End to End Data Science in Python with Dask

https://www.kdnuggets.com/2020/11/pandas-steroids-dask-python-data-science.html

How to Work with BIG Datasets : https://www.kaggle.com/yuliagm/how-to-work-with-big-datasets-on-16g-ram-dask

@ai_machinelearning_big_data

DGL is an easy-to-use, high performance and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning if a deep graph model is a component of an end-to-end application, the rest of the logics can be implemented in any major frameworks, such as PyTorch, Apache MXNet or TensorFlow.

#python, #scalable, #deeplearning, #dl, #graphs

That’s really interesting, take a look at this interview with... AI....

https://youtu.be/PqbB07n_uQ4

Learning to Summarize from Human Feedback

Github: https://github.com/openai/summarize-from-feedback

Dataset: https://openaipublic.blob.core.windows.net/summarize-from-feedback/website/index.html#/

Paper: https://proceedings.neurips.cc//paper/2020/file/1f89885d556929e98d3ef9b86448f951-Paper.pdf

@ai_machinelearning_big_data

Fairseq is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks.

#python, #sequence, #modeling, #toolkit, #ai

​​Animating Pictures with Eulerian Motion Fields

New method for single image animation. Authors promised to release code soon!

Website: https://eulerian.cs.washington.edu
Paper: https://eulerian.cs.washington.edu/animating_pictures_2020.pdf
ArXiV: https://arxiv.org/abs/2011.15128
YouTube: https://www.youtube.com/watch?v=4zKliOMilGY

#DL #animation #WashingtonUni

Yandex Team Talk at NeurIPS. Talk will be most interesting for those who are working on critical aspects of successful data collection and labeling.

Moderation team will focus on:
- Remoteness. A discussion about effectiveness and efficiency of remote work on crowdsourcing platforms.
- Fairness. How the working environment (e.g., a crowdsourcing platform) may help provide executors flexibility in choosing/switching tasks and working hours.
- Mechanisms. Discussion on bilateral mechanisms that not only provide flexibility to the performers, but also guarantee the quality of the result and the efficiency of the process to the customers.

Toloka's workshop info: https://clck.ru/SNwi3

#NeurIPS2020 #labeling #Yandex

Sketch-Generation-with-Drawing-Process-Guided-by-Vector-Flow-and-Grayscale

Github: https://github.com/TZYSJTU/Sketch-Generation-with-Drawing-Process-Guided-by-Vector-Flow-and-Grayscale

Paper: https://arxiv.org/abs/2012.09004

@ai_machinelearning_big_data

SOREL-20M
https://github.com/sophos-ai/SOREL-20M

Project Page:
https://github.com/TZYSJTU/Sketch-Generation-with-Drawing-Process-Guided-by-Vector-Flow-and-Grayscale

Paper:
https://arxiv.org/pdf/2012.09004.pdf

Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained.

The purpose of this repository is not to implement machine learning algorithms by using 3rd party library one-liners but rather to practice implementing these algorithms from scratch and get better understanding of the mathematics behind each algorithm. That's why all algorithms implementations are called "homemade" and not intended to be used for production.

#python, #machine, #learning, #ml, #jupyter, #notebooks, #algorithms

OpenBot leverages smartphones as brains for low-cost robots. We have designed a small electric vehicle that costs about $50 and serves as a robot body. Our software stack for Android smartphones supports advanced robotics workloads such as person following and real-time autonomous navigation.

#java, #jvm, #android, #robot, #robotics, #education, #research

🤗 multilingual datasets

- 611 datasets you can download in one line of python;
- 467 languages covered, 99 with at least 10 datasets;
- efficient pre-processing to free you from memory constraints;

https://github.com/huggingface/datasets