d2l-en
99-ML-Learning-Projects
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| d2l-en | 99-ML-Learning-Projects | |
|---|---|---|
| 6 | 1 | |
| 21,564 | 555 | |
| 2.8% | - | |
| 8.7 | 0.0 | |
| about 1 month ago | 2 months ago | |
| Python | Jupyter Notebook | |
| GNU General Public License v3.0 or later | MIT License |
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.
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.
99-ML-Learning-Projects
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I created a way to learn machine learning through Jupyter
Looks cool. Also sounds like it would fit will with the 99 ML Projects repo. Maybe you could contribute here https://github.com/gimseng/99-ML-Learning-Projects
What are some alternatives?
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
PySyft - Perform data science on data that remains in someone else's server
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
cortx - CORTX Community Object Storage is 100% open source object storage uniquely optimized for mass capacity storage devices.
TF-Watcher - Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle
FinMind - Open Data, more than 50 financial data. 提供超過 50 個金融資料(台股為主)，每天更新 https://finmind.github.io/
imbalanced-regression - [ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
Python Cheatsheet - All-inclusive Python cheatsheet
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.
Practical_RL - A course in reinforcement learning in the wild
ssd_keras - A Keras port of Single Shot MultiBox Detector
100DaysofMLCode - My journey to learn and grow in the domain of Machine Learning and Artificial Intelligence by performing the #100DaysofMLCode Challenge. Now supported by bright developers adding their learnings :+1: