ml-course VS TabularSemanticParsing

Compare ml-course vs TabularSemanticParsing and see what are their differences.

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ml-course TabularSemanticParsing
8 1
2,059 215
2.4% 0.9%
2.4 0.0
3 days ago 11 months ago
Jupyter Notebook Jupyter Notebook
MIT License BSD 3-clause "New" or "Revised" License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

ml-course

Posts with mentions or reviews of ml-course. We have used some of these posts to build our list of alternatives and similar projects.

TabularSemanticParsing

Posts with mentions or reviews of TabularSemanticParsing. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing ml-course and TabularSemanticParsing you can also consider the following projects:

pytorch-implementations - A collection of paper implementations using the PyTorch framework

ecco - Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).

IJCAI2023-CoNR - IJCAI2023 - Collaborative Neural Rendering using Anime Character Sheets

fake-news - Building a fake news detector from initial ideation to model deployment

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.

pytorch-sentiment-analysis - Tutorials on getting started with PyTorch and TorchText for sentiment analysis.

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.

Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.

adaptnlp - An easy to use Natural Language Processing library and framework for predicting, training, fine-tuning, and serving up state-of-the-art NLP models.

Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.

bert-sklearn - a sklearn wrapper for Google's BERT model