Hello colleagues!

Today I would like to share great news with you - we have opensourced our python framework LightAutoML (LAMA) aimed at Automated Machine Learning. It is designed to be lightweight and efficient for various tasks (binary/multiclass classifcation and regression) on tabular datasets which contains different types of features: numeric, categorical, dates, texts etc.

LAMA provides not only presets suite for end-to-end ML tasks solving, but also the easy-to-use ML pipeline creation constructor including data preprocessing elements, advanced feature generation, CV schemes (including nested CVs), hyperparameters tuning, different models and composition building methods. It also gives the user an option to generate model training and profiling reports to check model results and find insights which are not obvious from initial dataset.

Here are some examples of LAMA usage on binary classification task:
⁃ Blackbox pipeline = https://www.kaggle.com/simakov/lama-tabularautoml-preset-example
⁃ Interpretable model = https://www.kaggle.com/simakov/lama-whitebox-preset-example
⁃ Custom elements + existing ones = https://www.kaggle.com/simakov/lama-custom-automl-pipeline-example

Official documentation is here: https://lightautoml.readthedocs.io
Github: https://github.com/sberbank-ai-lab/LightAutoML
Slack community: https://lightautoml-slack.herokuapp.com

Please enjoy! :)

River: machine learning for streaming data in Python

https://github.com/online-ml/river

Paper: https://arxiv.org/abs/2012.04740v1

Online machine learning: https://www.wikiwand.com/en/Online_machine_learning

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Implementation of Adam and AMSGrad Optimizers

Project: https://lab-ml.com/labml_nn/optimizers/adam.html

AMSGrad: https://lab-ml.com/labml_nn/optimizers/amsgrad.html

GitHub: https://github.com/lab-ml/nn

Full list: https://lab-ml.com/labml_nn/index.html

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A Rising Library Beating Pandas in Performance

https://www.kdnuggets.com/2020/12/rising-library-beating-pandas-performance.html

Dataset: https://www.kaggle.com/colinmorris/reddit-usernames

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ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation

Github: https://github.com/joe-siyuan-qiao/ViP-DeepLab

Dataset: http://semantic-kitti.org

Paper: https://arxiv.org/abs/2012.05258v1

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Fairscale is a PyTorch extension library for high performance and large scale training for optimizing training

Github: https://github.com/facebookresearch/fairscale

Documentation: https://fairscale.readthedocs.io/en/latest/

Tutorials: https://fairscale.readthedocs.io/en/latest/tutorials/index.html

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Real-Time High-Resolution Background Matting

https://grail.cs.washington.edu/projects/background-matting-v2/

Github: https://github.com/PeterL1n/BackgroundMattingV2

Paper: https://arxiv.org/abs/2012.07810v1

Video: https://www.youtube.com/watch?v=oMfPTeYDF9g

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RLax -useful building blocks for implementing reinforcement learning agents

Github: https://github.com/deepmind/rlax

deepmind article: https://deepmind.com/blog/article/using-jax-to-accelerate-our-research

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Multi-worker training with Keras

https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras

Code: https://github.com/tensorflow/docs/blob/master/site/en/tutorials/distribute/multi_worker_with_keras.ipynb

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Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting.

Github: https://github.com/zhouhaoyi/Informer2020

Paper: https://arxiv.org/abs/2012.07436v1

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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

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🌐 Optimization Algorithms in Neural Networks

https://datascience-enthusiast.com/DL/Optimization_methods.html

Most used optimizers: https://www.theaidream.com/post/optimization-algorithms-in-neural-networks

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Learning Continuous Image Representation with Local Implicit Image Function.

https://yinboc.github.io/liif/

Github: https://github.com/yinboc/liif

Video: https://www.youtube.com/watch?v=6f2roieSY_8

Paper: https://arxiv.org/abs/2012.09161

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🔮 An end-to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku

Article: https://towardsdatascience.com/an-end-to-end-machine-learning-project-with-python-pandas-keras-flask-docker-and-heroku-c987018c42c7

Habr Ru: https://habr.com/ru/company/skillfactory/blog/534078

Code: https://github.com/RyanEricLamb/rugby-score-prediction

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🎇 TensorFlow Tutorial

https://datascience-enthusiast.com/DL/Tensorflow_Tutorial.html

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YolactEdge: Real-time Instance Segmentation on the Edge

Github: https://github.com/haotian-liu/yolact_edge

Demo: https://www.youtube.com/watch?v=GBCK9SrcCLM

Paper: https://arxiv.org/abs/2012.12259

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🔥 DeiT: Data-efficient Image Transformers

Gittub: https://github.com/facebookresearch/deit

Facebook’s research: https://ai.facebook.com/blog/data-efficient-image-transformers-a-promising-new-technique-for-image-classification/

Paper: https://arxiv.org/abs/2012.12877v1

Vision Transformer: https://github.com/lucidrains/vit-pytorch

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🌐 Global Context Networks

Github: https://github.com/xvjiarui/GCNet

Paper: https://arxiv.org/abs/2012.13375v1


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