PDEBench
d2l-en
PDEBench | d2l-en | |
---|---|---|
2 | 6 | |
623 | 21,704 | |
3.7% | 1.3% | |
6.5 | 8.5 | |
about 1 month ago | 11 days ago | |
Python | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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PDEBench
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[P] LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite
LagrangeBench is a machine learning benchmarking library for CFD particle problems based on JAX. It is designed to evaluate and develop learned particle models (e.g. graph neural networks) on challenging physical problems. To our knowledge it's the first benchmark for this specific set of problems. Our work was inspired by the grid-based benchmarks of PDEBench and PDEArena, and we propose it as a Lagrangian alternative.
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[D] what are the SOTA neural PDE solvers besides FNO?
try https://github.com/pdebench/pdebench
d2l-en
- which book to chose for deep learning :lan Goodfellow or francois chollet
- d2l-en: Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.
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How to pre-train BERT on different objective tasks using HuggingFace
There might is bert library for pre-train bert model in huggingface, But I suggestion that you train bert model in native pytorch to understand detail, Limu's course is recommended for you
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The Transformer in Machine Translation
GitHub's article on Dive into Deep Learning
- D2l-En
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I created a way to learn machine learning through Jupyter
There are actually some online books and courses built on Jupyter Notebook ([Dive to Deep Learning Book](https://github.com/d2l-ai/d2l-en) for example). However yours is more detail and could really helps beginners.
What are some alternatives?
squirrel-core - A Python library that enables ML teams to share, load, and transform data in a collaborative, flexible, and efficient way :chestnut:
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
ivy - The Unified Machine Learning Framework [Moved to: https://github.com/unifyai/ivy]
TF-Watcher - Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle
pdearena
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
imbalanced-regression - [ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
petastorm - Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.