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	    				<h2>Bridge the gap between algorithms and implementations of deep learning</h2>
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	      					<h3>Powerful</h3>
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                				Chainer supports CUDA computation.
				                It only requires few lines of codes to leverage a GPU.
                				It also runs on multiple GPUs with a little effort.
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	      					<h3>Flexible</h3>
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								Chainer supports various network architectures including feed-forward nets, convnets, recurrent nets and recursive nets. It also supports per-batch architectures.
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	      					<h3>Intuitive</h3>
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								Forward computation can include any control flow statements of Python without lacking ability of backpropagation. It makes codes intuitive and easy to debug.
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	      					<h2>Quick Start</h2>
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	      					Install Chainer:<br>
	      					<pre><code>pip install chainer</code></pre>
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	      					Run the MNIST example:<br>
	      					<pre><code>wget https://github.com/pfnet/chainer/archive/v{{ site.chainer_version }}.tar.gz
tar xzf v{{ site.chainer_version }}.tar.gz
python chainer-{{ site.chainer_version }}/examples/mnist/train_mnist.py</code></pre>
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	      					Learn more from <a href="http://docs.chainer.org">the official documentation</a>.
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            			<a class="twitter-timeline" href="https://twitter.com/ChainerOfficial" data-widget-id="606658917934858240">@ChainerOfficial</a>
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				<h2>Slides</h2>
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              				<a href="http://www.slideshare.net/beam2d/introduction-to-chainer-a-flexible-framework-for-deep-learning" class="image fit" style="border: 1px solid #ccc"><img src="http://image.slidesharecdn.com/pfiseminarchainer-150618075108-lva1-app6891/95/slide-1-1024.jpg"/></a>
              				<p><a href="http://www.slideshare.net/beam2d/introduction-to-chainer-a-flexible-framework-for-deep-learning">Introduction to Chainer: A Flexible Framework for Deep Learning</a></p>
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