With TensorFlow, deep machine learning has transitioned from an area of research into mainstream software engineering. Martin Görner walks you through building and training a neural network that recognizes handwritten digits with >99% accuracy using Python and TensorFlow. Along the way, Martin discusses many standard deep learning techniques such as minibatching, learning rate decay, dropout, convolutional networks, and more and demonstrates how to implement them in TensorFlow.
Martin Görner works with developer relations at Google. Martin is passionate about science, technology, coding, algorithms, and everything in between. Previously, he worked in the computer architecture group of ST Microlectronics and spent 11 years shaping the nascent ebook market, starting at Mobipocket, a startup that later became the software part of the Amazon Kindle and its mobile variants. He graduated from Mines Paris Tech.
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