Our life is frittered away by detail. Simplify, simplify. –
Henry David Thoreau
This short book teaches you how you can build machine learning applications (with
Leaf is a Machine Intelligence Framework engineered by hackers, not scientists.
It has a very simple API consisting of Layers and Solvers, with which
you can build classical machine as well as deep learning and other fancy machine
intelligence applications. Although Leaf is just a few months old,
thanks to Rust and Collenchyma it is already one of the fastest machine intelligence
Leaf was inspired by the brilliant people behind TensorFlow, Torch, Caffe,
Rust and numerous research papers and brings modularity, performance and
portability to deep learning.
To make the most of the book, a basic understanding of the fundamental concepts
of machine and deep learning is recommended. Good resources to get you from
zero to almost-ready-to-build-machine-learning-applications:
Both machine and deep learning are really easy with Leaf.
Construct a Network by chaining Layers.
Then optimize the network by feeding it examples.
This is why Leaf’s entire API consists of only two concepts: Layers
and Solvers. Use layers to construct almost any kind of model: deep,
classical, stochastic or hybrids, and solvers for executing and optimizing the
Leaf was built with three concepts in mind: accessibility/simplicity,
performance and portability. We want developers and companies to be able to
run their machine learning applications anywhere: on servers, desktops,
smartphones and embedded devices. Any combination of platform and
computation language (OpenCL, CUDA, etc.) is a first class citizen in Leaf.
We coupled portability with simplicity, meaning you can deploy your machine
learning applications to almost any machine and device with no code changes.
Learn more at chapter 4. Backend or at the
Collenchyma Github repository.
Want to contribute? Awesome!
We have instructions to help you get started.
Alongside this book you can also read the Rust API documentation if
you would like to use Leaf as a crate, write a library on top of it or
just want a more low-level overview.
Original URL: http://feedproxy.google.com/~r/feedsapi/BwPx/~3/v7nMIe3TKto/leaf.html