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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/"
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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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          <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>
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