client
d2l-en
| client | d2l-en | |
|---|---|---|
| 2 | 6 | |
| 90 | 21,704 | |
| - | 1.3% | |
| 9.8 | 8.5 | |
| about 5 hours ago | 10 days ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
client
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It was not "Good First Issue"
it was really all there, so what did I do?, I commented took the issue Issue 366 I mean it sounded simple enough, update a function so that we can downlaod all data with no arguments involved.
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[P] Stream and Upload Versioned Data
Check out the official project https://github.com/dagshub/client
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?
features - A collection of development container 'features' for machine learning and data science
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
horovod - Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. [Moved to: https://github.com/horovod/horovod]
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
analog-watch-recognition - Reading time from analog clocks
TF-Watcher - Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle
pubmedflow - Data Collection API for pubmed
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
igel - a delightful machine learning tool that allows you to train, test, and use models without writing code
imbalanced-regression - [ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
ludwig - Low-code framework for building custom LLMs, neural networks, and other AI models
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.