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Leaf
Leaf

15
27
+ 1
0
Open Data Hub
Open Data Hub

1
9
+ 1
0
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Leaf vs Open Data Hub: What are the differences?

Leaf: Machine learning framework in Rust. Leaf is a Machine Intelligence Framework engineered by software developers, not scientists. It was inspired by the brilliant people behind TensorFlow, Torch, Caffe, Rust and numerous research papers and brings modularity, performance and portability to deep learning. Leaf is lean and tries to introduce minimal technical debt to your stack; Open Data Hub: An open source project that provides open source AI tools for running large and distributed AI workloads on OpenShift Container Platform. It is an open source project that provides open source AI tools for running large and distributed AI workloads on OpenShift Container Platform. Currently, It provides open source tools for data storage, distributed AI and Machine Learning (ML) workflows and a Notebook development environment.

Leaf and Open Data Hub can be primarily classified as "Machine Learning" tools.

Leaf is an open source tool with 5.42K GitHub stars and 273 GitHub forks. Here's a link to Leaf's open source repository on GitHub.

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What is Leaf?

Leaf is a Machine Intelligence Framework engineered by software developers, not scientists. It was inspired by the brilliant people behind TensorFlow, Torch, Caffe, Rust and numerous research papers and brings modularity, performance and portability to deep learning. Leaf is lean and tries to introduce minimal technical debt to your stack.

What is Open Data Hub?

It is an open source project that provides open source AI tools for running large and distributed AI workloads on OpenShift Container Platform. Currently, It provides open source tools for data storage, distributed AI and Machine Learning (ML) workflows and a Notebook development environment.
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              What are some alternatives to Leaf and Open Data Hub?
              TensorFlow
              TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
              Keras
              Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
              scikit-learn
              scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
              PyTorch
              PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
              ML Kit
              ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
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