Ludwig is a TensorFlow-based toolbox that allows to train and test deep learning models without the need to write code.
Why :
No coding required: no coding skills are required to train a model and use it for obtaining predictions. Generality: a new data type-based approach to deep learning model design that makes the tool usable across many different use cases. Flexibility: experienced users have extensive control over model building and training, while newcomers will find it easy to use. Extensibility: easy to add new model architecture and new feature data types. Understandability: deep learning model internals are often considered black boxes, but we provide standard visualizations to understand their performance and compare their predictions. Open Source: Apache License 2.0
--
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🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.
## 📋 Pull Request Information
**Original PR:** https://github.com/vinta/awesome-python/pull/1243
**Author:** [@Naveen-Zerocool](https://github.com/Naveen-Zerocool)
**Created:** 2/25/2019
**Status:** ❌ Closed
**Base:** `master` ← **Head:** `master`
---
### 📝 Commits (1)
- [`8a35e27`](https://github.com/vinta/awesome-python/commit/8a35e27d7686d3017169601b596827c5b8a66405) Update README.md
### 📊 Changes
**1 file changed** (+1 additions, -0 deletions)
<details>
<summary>View changed files</summary>
📝 `README.md` (+1 -0)
</details>
### 📄 Description
## Ludwig
Ludwig is a TensorFlow-based toolbox that allows to train and test deep learning models without the need to write code.
## Why :
_No coding required_: no coding skills are required to train a model and use it for obtaining predictions.
_Generality_: a new data type-based approach to deep learning model design that makes the tool usable across many different use cases.
_Flexibility_: experienced users have extensive control over model building and training, while newcomers will find it easy to use.
_Extensibility_: easy to add new model architecture and new feature data types.
_Understandability_: deep learning model internals are often considered black boxes, but we provide standard visualizations to understand their performance and compare their predictions.
_Open Source_: Apache License 2.0
--
Anyone who agrees with this pull request could vote for it by adding a :+1: to it, and usually, the maintainer will merge it when votes reach **20**.
---
<sub>🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.</sub>
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📋 Pull Request Information
Original PR: https://github.com/vinta/awesome-python/pull/1243
Author: @Naveen-Zerocool
Created: 2/25/2019
Status: ❌ Closed
Base:
master← Head:master📝 Commits (1)
8a35e27Update README.md📊 Changes
1 file changed (+1 additions, -0 deletions)
View changed files
📝
README.md(+1 -0)📄 Description
Ludwig
Ludwig is a TensorFlow-based toolbox that allows to train and test deep learning models without the need to write code.
Why :
No coding required: no coding skills are required to train a model and use it for obtaining predictions.
Generality: a new data type-based approach to deep learning model design that makes the tool usable across many different use cases.
Flexibility: experienced users have extensive control over model building and training, while newcomers will find it easy to use.
Extensibility: easy to add new model architecture and new feature data types.
Understandability: deep learning model internals are often considered black boxes, but we provide standard visualizations to understand their performance and compare their predictions.
Open Source: Apache License 2.0
--
Anyone who agrees with this pull request could vote for it by adding a 👍 to it, and usually, the maintainer will merge it when votes reach 20.
🔄 This issue represents a GitHub Pull Request. It cannot be merged through Gitea due to API limitations.