python-small-examples
d2l-en
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| python-small-examples | d2l-en | |
|---|---|---|
| 1 | 6 | |
| 7,833 | 21,704 | |
| - | 3.1% | |
| 4.5 | 8.5 | |
| 8 months ago | 6 days ago | |
| Python | Python | |
| - | GNU General Public License v3.0 or later |
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python-small-examples
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Ask HN: What are the best tutorial sites for Python?
All these are neither recent (they are updated though) nor AI related. Here you go :
If you are new to dev, I like "think python"
If you are already into programming, I personally find sifting through organized small code snippets helpful for learning a new language :
https://github.com/jackzhenguo/python-small-examples
https://github.com/satwikkansal/wtfpython/blob/master/README...
https://www.30secondsofcode.org/python/p/1
Finally, "The Hitchhiker’s Guide to Python" is good for everything configuration, and usage.
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?
wtfpython - What the f*ck Python? 😱
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
TF-Watcher - Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle
best-of-ml-python - 🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
imbalanced-regression - [ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
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.
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
learning-topology-synthetic-data - Tensorflow implementation of Learning Topology from Synthetic Data for Unsupervised Depth Completion (RAL 2021 & ICRA 2021)