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.
Leaf is a tool in the Machine Learning Tools category of a tech stack.
Leaf is an open source tool with 5.4K GitHub stars and 273 GitHub forks. Here’s a link to Leaf's open source repository on GitHub
Who uses Leaf?
4 companies reportedly use Leaf in their tech stacks, including Growstuff, jobroom2, and Sensego.
9 developers on StackShare have stated that they use Leaf.
Why developers like Leaf?
Here’s a list of reasons why companies and developers use Leaf
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Leaf Alternatives & Comparisons
What are some alternatives to Leaf?
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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.
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
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 brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.