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François Chollet, Deep Learning with Python (2018).zip
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If you’ve picked up this book, you’re probably aware of the extraordinary progress
that deep learning has represented for the field of artificial intelligence in the recent
past. In a mere five years, we’ve gone from near-unusable image recognition and
speech transcription, to superhuman performance on these tasks.
The consequences of this sudden progress extend to almost every industry. But in
order to begin deploying deep-learning technology to every problem that it could
solve, we need to make it accessible to as many people as possible, including non
experts—people who aren’t researchers or graduate students. For deep learning to
reach its full potential, we need to radically democratize it.
When I released the first version of the Keras deep-learning framework in March
2015, the democratization of AI wasn’t what I had in mind. I had been doing research
in machine learning for several years, and had built Keras to help me with my own
experiments. But throughout 2015 and 2016, tens of thousands of new people
entered the field of deep learning; many of them picked up Keras because it was—and
still is—the easiest framework to get started with. As I watched scores of newcomers
use Keras in unexpected, powerful ways, I came to care deeply about the accessibility
and democratization of AI. I realized that the further we spread these technologies,
the more useful and valuable they become. Accessibility quickly became an explicit
goal in the development of Keras, and over a few short years, the Keras developer
community has made fantastic achievements on this front. We’ve put deep learning
into the hands of tens of thousands of people, who in turn are using it to solve import
ant problems we didn’t even know existed until recently.
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