ml-course
d2l-en
| ml-course | d2l-en | |
|---|---|---|
| 8 | 6 | |
| 2,075 | 22,174 | |
| 0.9% | 2.1% | |
| 2.1 | 8.0 | |
| 20 days ago | 10 days ago | |
| Jupyter Notebook | 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.
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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.
ml-course
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?
pytorch-implementations - A collection of paper implementations using the PyTorch framework
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
Subway-Station-Hazard-Detection - This project is part of the CS course 'Systems Engineering Meets Life Sciences II' at Goethe University Frankfurt. In this Computer Vision project, we developed a first prototype of a security system which uses the surveillance cameras at subway stations to recognize dangerous situations. The training data was artificially generated by a Unity-based simulation.
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
IJCAI2023-CoNR - IJCAI2023 - Collaborative Neural Rendering using Anime Character Sheets
TF-Watcher - Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle
TabularSemanticParsing - Translating natural language questions to a structured query language
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
Deep-Learning-Computer-Vision - My assignment solutions for Stanford’s CS231n (CNNs for Visual Recognition) and Michigan’s EECS 498-007/598-005 (Deep Learning for Computer Vision), version 2020.
imbalanced-regression - [ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
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.