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<!DOCTYPE html>
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<!-- Last Published: Thu Mar 05 2020 21:27:03 GMT+0000 (Coordinated Universal Time) -->
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content="Larq is an ecosystem of open-source Python packages for building, training and deploying Binarized Neural Networks to enable efficient inference on mobile and edge devices.">
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content="Larq is an ecosystem of open-source Python packages for building, training and deploying Binarized Neural Networks to enable efficient inference on mobile and edge devices.">
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<h2>Binarized Neural Networks for efficient deep learning<br></h2>
<p>Larq is an ecosystem of open-source Python packages for building, training and deploying Binarized Neural
Networks to
enable efficient inference on mobile and edge devices.</p><a href="https://docs.larq.dev/"
class="button w-button">Get started with Larq</a>
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<h2 class="heading-narrow">Deep learning with 1-bit weights and activations<br></h2>
<p>Most neural networks use 32, 16 or 8 bits to encode each weight and activation, making them slow and
power-hungry. Binarized Neural Networks (BNNs) restrict weights and activations to be only +1 or -1, and
drastically reduce the model’s memory footprint and computational complexity.</p>
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<h2 class="heading-narrow">End-to-end tools for developing BNNs<br></h2>
<p>Larq lets engineers and researchers access state-of-the-art BNNs, train their own from scratch, and deploy them
on mobile and edge devices.</p>
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<h3>Ready to use pretrained models</h3>
<p><a href="https://docs.larq.dev/zoo" class="link-text">Larq Zoo</a> provides implementations and pretrained
weights for cutting-edge BNNs, allowing you to effortlessly start using efficient deep learning in your
projects.<br></p>
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<h3>Intuitive and flexible extension of TensorFlow Keras</h3>
<p><a href="https://docs.larq.dev" class="link-text">Larq</a> is a powerful yet easy-to-use library for
building and training BNNs that is fully compatible with the larger tf.keras ecosystem.<br></p>
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<div class="div-block">
<h3>Simple deployment for the fastest inference</h3>
<p><a href="https://docs.larq.dev/compute-engine" class="link-text">Larq Compute Engine</a> is a
highly-optimized inference library for deploying BNNs on mobile and edge devices.<br></p>
</div>
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<h3 class="resource-meta">LEARN LARQ</h3>
<h2 class="resource-title">Introduction to BNNs with Larq</h2><img src="images/model.png" width="520"
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<h3 class="resource-meta">LEARN LARQ</h3>
<h2 class="resource-title">Deploy your first BNN on Android</h2><img src="images/spot-embed.png" width="480"
srcset="images/spot-embed-p-500.png 500w, images/spot-embed-p-800.png 800w, images/spot-embed.png 883w"
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