〰 Monotonic Differentiable Sorting Networks

Github: https://github.com/Felix-Petersen/diffsort

Colab: https://colab.research.google.com/drive/1q0TZFFYB9FlOJYWKt0_7ZaXQT190anhm?usp=sharing

Paper: https://arxiv.org/abs/2203.09630v1

Video: https://www.youtube.com/watch?v=Rl-sFaE1z4M

Dataset: https://paperswithcode.com/dataset/svhn

Генеративное моделирование путем оценки градиентов распределения данных.

https://yang-song.github.io/blog/2021/score/

👉 @bigdata_1

xmanager: фреймворк для управления экспериментами в DS.

https://github.com/deepmind/xmanager

👉 @bigdata_1

Как я стал настоящим самоучкой в Data Science: моя история.

https://towardsdatascience.com/how-i-became-an-actual-self-taught-data-scientist-my-story-ba4dbeb96cf8

👉 @bigdata_1

Реализация эффекта Моны Лизы с помощью tensorflow.

https://blog.tensorflow.org/2020/09/bringing-mona-lisa-effect-to-life-tensorflow-js.html

👉 @bigdata_1

Отладка моделей машинного обучения с помощью Comet Artifacts.

https://www.comet.ml/site/debugging-your-machine-learning-models-with-comet-artifacts/

👉 @bigdata_1

Советы и улучшения Jupyter Notebook

https://nathaniel-speiser.github.io/Jupyter-Notebook-tips-and-improvements/

👉 @bigdata_1

Как освоить Streamlit для науки о данных.

https://blog.streamlit.io/how-to-master-streamlit-for-data-science/

👉 @bigdata_1

Graph Transformer Architecture

Github: https://github.com/graphdeeplearning/graphtransformer

Paper: https://arxiv.org/abs/2012.09699

👉 @bigdata_1

🕹 Mastering Atari with Discrete World Models

Github: https://github.com/danijar/dreamerv2

Google research: https://ai.googleblog.com/2021/02/mastering-atari-with-discrete-world.html

Paper: https://arxiv.org/abs/2010.02193

👉 @bigdata_1

Efficient CNN-LSTM based Image Captioning using Neural Network Compression

Github: https://github.com/amanmohanty/idl-nncompress

Paper: https://arxiv.org/pdf/2012.09708.pdf

👉 @bigdata_1

Unbiased Teacher for Semi-Supervised Object Detection

Github: https://github.com/facebookresearch/unbiased-teacher

Paper: https://arxiv.org/abs/2102.09480

Project: https://ycliu93.github.io/projects/unbiasedteacher.html

👉 @bigdata_1

Deep Learning & Art: Neural Style Transfer

https://datascience-enthusiast.com/DL/Art_Generation_with_Neural_Style_Transfer_v2.html

👉 @bigdata_1

🔥 Model Search by Google

Automatically build and deploy state-of-the-art machine learning models on structured data.

Github: https://github.com/google/model_search

Paper: https://pdfs.semanticscholar.org/1bca/d4cdfbc01fbb60a815660d034e561843d67a.pdf

Project: https://cloud.google.com/automl-tables

👉 @bigdata_1

Domain specific BERT representation for Named Entity Recognition of lab protocol

Github: https://github.com/tejasvaidhyadev/NER_Lab_Protocols

Website: http://noisy-text.github.io/2020/wlp-task.html

Paper: https://arxiv.org/pdf/2012.11145.pdf

👉 @bigdata_1

🚀 DALL-E Zero-Shot Text-to-Image Generation

Github: https://github.com/openai/DALL-E

Paper: https://arxiv.org/abs/2102.12092

OpenAi: https://openai.com/blog/dall-e/

👉 @bigdata_1

RECCON: Recognizing Emotion Cause in CONversations

Github: https://github.com/declare-lab/RECCON

Paper: https://arxiv.org/pdf/2012.11820.pdf

👉 @bigdata_1

‍Полный курс по искусственному интеллекту CS221 от университета Stanford
Stanford CS221: Artificial Intelligence: Principles and Techniques | Autumn 2019

Lecture 1: Overview
Lecture 2: Machine Learning 1 - Linear Classifiers, SGD
Lecture 3: Machine Learning 2 - Features, Neural Networks
Lecture 4: Machine Learning 3 - Generalization, K-means
Lecture 5: Search 1 - Dynamic Programming, Uniform Cost Search
Lecture 6: Search 2 - A*
Lecture 7: Markov Decision Processes - Value Iteration
Lecture 8: Markov Decision Processes - Reinforcement Learning
Lecture 9: Game Playing 1 - Minimax, Alpha-beta Pruning
Lecture 10: Game Playing 2 - TD Learning, Game Theory
Lecture 11: Factor Graphs 1 - Constraint Satisfaction Problems
Lecture 12: Factor Graphs 2 - Conditional Independence
Lecture 13: Bayesian Networks 1 - Inference
Lecture 14: Bayesian Networks 2 - Forward-Backward
Lecture 15: Bayesian Networks 3 - Maximum Likelihood
Lecture 16: Logic 1 - Propositional Logic
Lecture 17: Logic 2 - First-order Logic
Lecture 18: Deep Learning
Lecture 19: Conclusion

https://www.youtube.com/playlist?list=PLoROMvodv4rO1NB9TD4iUZ3qghGEGtqNX


👉 @bigdata_1

When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute

Github: https://github.com/asappresearch/sru

Paper: https://arxiv.org/abs/2102.12459v1

Project: https://www.asapp.com/blog/reducing-the-high-cost-of-training-nlp-models-with-sru/

👉 @bigdata_1