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<ul class="chapter"><li><a href="./juice.html" class="active"><strong>1.</strong> Juice</a></li><li><a href="./layers.html"><strong>2.</strong> Layers</a></li><li><ul class="section"><li><a href="./layer-lifecycle.html"><strong>2.1.</strong> Layer Lifecycle</a></li><li><a href="./building-networks.html"><strong>2.2.</strong> Create a Network</a></li><li><a href="./create-new-layer.html"><strong>2.3.</strong> Create a new Layer</a></li></ul></li><li><a href="./solvers.html"><strong>3.</strong> Solvers</a></li><li><ul class="section"><li><a href="./optimize-layers.html"><strong>3.1.</strong> Optimize Layers</a></li><li><a href="./multi-device-optimization.html"><strong>3.2.</strong> Multi-Device Optimization</a></li><li><a href="./distributed-optimization.html"><strong>3.3.</strong> Distributed Optimization</a></li></ul></li><li><a href="./backend.html"><strong>4.</strong> Backend</a></li><li><a href="./deep-learning-glossary.html"><strong>5.</strong> Glossary</a></li><li class="spacer"></li><li><a href="http://spearow.github.io/juice/juice/index.html"><strong>6.</strong> Rust API Documentation</a></li></ul>
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<h1>Juice - Machine Learning for Hackers</h1>
<blockquote>
<p>Our life is frittered away by detail. Simplify, simplify. -
<em>Henry David Thoreau</em></p>
</blockquote>
<p>This short book teaches you how you can build machine learning applications (with
<a href="https://github.com/spearow/juice">Juice</a>).</p>
<p>Juice is a Machine Intelligence Framework engineered by hackers, not scientists.
It has a very simple API consisting of <a href="./layers.html">Layers</a> and <a href="./solvers.html">Solvers</a>, with which
you can build classical machine as well as deep learning and other fancy machine
intelligence applications. Although Juice is just a few months old,
thanks to Rust and Coaster it is already one of the fastest machine intelligence
frameworks available.</p>
<p>Juice was inspired by the brilliant people behind TensorFlow, Torch, Caffe,
Rust and numerous research papers and brings modularity, performance and
portability to deep learning.</p>
<p><br/></p>
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<p><br/></p>
<blockquote>
<p>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:</p>
<ul>
<li><a href="http://neuralnetworksanddeeplearning.com/">Neural Networks and Deep Learning</a> or</li>
<li><a href="http://cs231n.github.io/">Stanford Course on (Convolutional) Neural Networks</a></li>
</ul>
<p>And if you already have some experience, <a href="http://www.andreykurenkov.com/writing/a-brief-history-of-neural-nets-and-deep-learning/">A 'brief' history of Deep Learning</a> or <a href="./deep-learning-glossary.html">The Glossary</a>
might prove informative.</p>
</blockquote>
<p>Both machine and deep learning are really easy with Juice.</p>
<p>Construct a <a href="./deep-learning-glossary.html#Network">Network</a> by chaining <a href="./deep-learning-glossary.html#Layer">Layers</a>.
Then optimize the network by feeding it examples.
This is why Juice's entire API consists of only two concepts: <a href="./layers.html">Layers</a>
and <a href="./solvers.html">Solvers</a>. Use layers to construct almost any kind of model: deep,
classical, stochastic or hybrids, and solvers for executing and optimizing the
model.</p>
<p>This is already the entire API for machine learning with Juice. To learn how
this is possible and how to build machine learning applications, refer to chapters
<a href="./layers.html">2. Layers</a> and <a href="./solvers.html">3. Solvers</a>. Enjoy!</p>
<h2>Benefits+</h2>
<p>Juice 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 Juice.</p>
<p>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 <a href="./backend.html">4. Backend</a> or at the
<a href="https://github.com/spearow/coaster">Coaster Github repository</a>.</p>
<h2>Contributing</h2>
<p>Want to contribute? Awesome!
<a href="https://github.com/spearow/juice/blob/master/CONTRIBUTING.md">We have instructions to help you get started</a>.</p>
<p>Juice has a near real-time collaboration culture, which happens at the <a href="https://github.com/spearow/juice">Github
repository</a> and on the
<a href="https://gitter.im/spearow/juice">Juice Gitter Channel</a>.</p>
<h2>API Documentation</h2>
<p>Alongside this book you can also read the Rust API documentation if
you would like to use Juice as a crate, write a library on top of it or
just want a more low-level overview.</p>
<p><a href="http://spearow.github.io/juice/">> Rust API documentation</a></p>
<h2>License</h2>
<p>Juice is free for anyone for whatever purpose.
Juice is licensed under either
<a href="https://github.com/spearow/juice/blob/master/LICENSE-APACHE">Apache License v2.0</a> or,
<a href="https://github.com/spearow/juice/blob/master/LICENSE-MIT">MIT license</a>.
Whatever strikes your fancy.</p>
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