pixelNeRF: Neural Radiance Fields from One or Few Images.

Github: https://github.com/sxyu/pixel-nerf

Paper: http://arxiv.org/abs/2012.02190

@ai_machinelearning_big_data

Tensorflow TPU

https://www.tensorflow.org/guide/tpu

Code: https://github.com/tensorflow/docs/blob/master/site/en/guide/tpu.ipynb

SavedModel Guide: https://www.tensorflow.org/guide/saved_model

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Learnable Tree Filter V2.

Github: https://github.com/StevenGrove/LearnableTreeFilterV2

Paper: https://arxiv.org/abs/2012.03482v1

@ai_machinelearning_big_data

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

@ai_machinelearning_big_data

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

@ArtificialIntelligencedl

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

@ai_machinelearning_big_data

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

@ai_machinelearning_big_data

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

@ai_machinelearning_big_data

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

@ai_machinelearning_big_data

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

@ai_machinelearning_big_data

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

@ai_machinelearning_big_data

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

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

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

@ai_machinelearning_big_data

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

🌐 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

@ai_machinelearning_big_data

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

@ai_machinelearning_big_data

🔮 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

@ai_machinelearning_big_data

🎇 TensorFlow Tutorial

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

@ai_machinelearning_big_data

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

@ai_machinelearning_big_data