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    <title>[untitled]</title>
    <dc:date>2016-06-23T10:42:03+00:00</dc:date>
    <link>http://autumnai.com/leaf/book/leaf.html</link>
    <dc:creator>sandbags</dc:creator><description><![CDATA[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 frameworks available.]]></description>
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    <dc:creator>sandbags</dc:creator><description><![CDATA[A few years ago, investors and startups were chasing “big data” (I helped put together a landscape on that industry). Now we’re seeing a similar explosion of companies calling themselves artificial intelligence, machine learning, or somesuch — collectively I call these “machine intelligence” (I’ll get into the definitions in a second). Our fund, Bloomberg Beta, which is focused on the future of work, has been investing in these approaches. I created this landscape to start to put startups into context. I’m a thesis-oriented investor and it’s much easier to identify crowded areas and see white space once the landscape has some sort of taxonomy.]]></description>
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    <title>Quoc Le’s Lectures on Deep Learning | Gaurav Trivedi</title>
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    <dc:creator>sandbags</dc:creator><description><![CDATA[The following introduction is to allow viewers to understand the basic concepts and practical implementation of neural nets towards a financial time series. I will not go too deep into detail about the mathematics behind the neural net at the moment. My goal is to get you to understand practical details about how to actually implement a neural net using simple tools and models. We will start with a simple model to understand a basic time series. The time series waveform is a simple sine wave with the period set to 30 days. It is implemented in excel as a source file to be processed in any Machine Learning capable software.]]></description>
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    <dc:creator>sandbags</dc:creator><description><![CDATA[An overview paper discussing some issues with time series analysis in particular classification and dimensional reduction techniques for similarity & indexing.]]></description>
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