Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

Ignite is a library that provides three high-level features:

- Extremely simple engine and event system
- Out-of-the-box metrics to easily evaluate models
- Built-in handlers to compose training pipeline, save artifacts and log parameters and metrics

#python, #pytorch, #ml, #neural, #network

KeenWrite: An Open Source Text Editor for Data Scientists
https://itsfoss.com/keenwrite/

Introduction to Pytorch Code Examples

An overview of training, models, loss functions and optimizers

Free course: https://cs230.stanford.edu/blog/pytorch/

Lectures: https://cs230.stanford.edu/lecture/

Github: https://github.com/thanhhff/CS230-Deep-Learning

@ai_machinelearning_big_data

Graph-based Neural Structured Learning in TFX

New learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs.

https://www.tensorflow.org/tfx/tutorials/tfx/neural_structured_learning

Article: https://blog.tensorflow.org/2020/10/neural-structured-learning-in-tfx.html

Neural Structured Learning: https://www.tensorflow.org/neural_structured_learning

Github: https://github.com/tensorflow/neural-structured-learning#videos-and-colab-tutorials

@ai_machinelearning_big_data

Open cool NOT Kaggle contest – AIJ Contest 2020 🎉

◼️ Digital Peter: Recognition of Peter the Great’s manuscripts
October 9 – November 8
Digital Peter is an educational task with a historical slant created on the basis of several AI technologies (CV, NLP, and knowledge graphs). The task was prepared jointly with the Saint Petersburg Institute of History of the Russian Academy of Sciences, Federal Archival Agency of Russia and the Russian State Archive of Ancient Acts.
Participants are invited to create an algorithm for line-by-line recognition of manuscripts written by Peter the Great.

◼️ NoFloodWithAI: Flash floods on the Amur river
October 9 – November 1
NoFloodWithAI is a special track with a socially important theme prepared jointly with the Ministry of Emergency Situations, Ministry of Natural Resources and Rosgidromet of Russia.
Participants are invited to develop an algorithm for short-term forecasting of water levels in the Amur River for the following settlements: Dzhalinda, Blagoveshchensk, Innokentievka, Leninskoye, Khabarovsk, Komsomolsk-on-Amur, Nikolaevsk-on-Amur for 10 days in advance in order to prevent emergency situations in Russia’s regions. The results of the contest will be reused to mitigate environmental risks and minimize economic damage wrought on the regions.


◼️ AI4Humanities: ruGPT-3
October 16 – November 1
It is a track developed especially for those familiar with artificial neural networks. It will help to learn about a promising ruGPT-3 technology able to generate very complex meaningful texts based on just one request made in natural human language. For example, it can help you answer most questions included in basic or unified national exams (OGE or EGE), write Java code at the request "Please make a website for the online shop", come up with a business idea for a new startup, or write new popular science articles.


more on the project page: https://ods.ai/tracks/aij2020

#ods #aij #contest #peter #floods #rugpt3

Free course: Deep Learning with Pytorch by Yann LeCun

En: https://atcold.github.io/pytorch-Deep-Learning

Ru: https://atcold.github.io/pytorch-Deep-Learning/ru/

GitHub: https://github.com/Atcold/pytorch-Deep-Learning

YouTube: https://www.youtube.com/playlist?list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq

@ai_machinelearning_big_data

Top deep learning Github repositories


https://github.com/mbadry1/Top-Deep-Learning

RecBole. A unified, comprehensive and efficient recommendation library

https://recbole.io/

Github: https://github.com/RUCAIBox/RecBole

Paper: https://arxiv.org/abs/2011.01731

Datasets: https://github.com/RUCAIBox/RecDatasets

📛PYTORCH CHATBOT TUTORIAL

https://pytorch.org/tutorials/beginner/chatbot_tutorial.html

Code: https://github.com/pytorch/tutorials/blob/master/beginner_source/chatbot_tutorial.py

Code for deep learning researchers to learn PyTorch:

https://github.com/yunjey/pytorch-tutorial

@ai_machinelearning_big_data

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