Tensorflow and deep learning - without a PhD by Martin Görner

Martin Görner explains the basics of deep learning and neural networks using TensorFlow, focusing on practical troubleshooting and MNIST classification.

Please subscribe to our YouTube channel @ bit.ly/devoxx-youtube Like us on Facebook @ facebook.com/devoxxcom Follow us on Twitter @ twitter.com/devoxx Google has recently open-sourced its framework for machine learning and neural networks called Tensorflow. With this new tool, deep machine learning transitions from an area of research into mainstream software engineering. In this session, we will teach you how to choose the right neural network for your problem and how to make it behave. Familiarity with differential equations is no longer required. Instead, a couple of lines of Tensorflow Python, and a bag of "tricks of the trade" will do the job. No previous Python knowledge required. This university session will cover the basics of deep learning, without any assumptions about the level of the participants. Machine learning beginners are welcome. We will cover: - fully connected neural networks - convolutional neural networks - regularisation techniques: dropout, learning rate decay, batch normalisation - recurrent neural networks - natural language analysis, word embeddings - transfer learning - image analysis - image generation - and many examples. Martin Görner is passionate about science, technology, coding, algorithms and everything in between. He graduated from Mines Paris Tech, enjoyed his first engineering years in the computer architecture group of ST Microlectronics and then spent the next 11 years shaping the nascent eBook market, starting with the Mobipocket startup, which later became the software part of the Amazon Kindle and its mobile variants. He joined Google Developer Relations in 2011 and now focuses on parallel processing and machine learning. [ULT-2698]