Google has recently open-sourced its framework for machine learning and neural networks called Tensorflow. In this session,you know 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.

In this video, Martin Gorner demonstrates how to construct and train a neural network that recognises handwritten digits. Along the way, he'll describe some "tricks of the trade" used in neural network design, and finally, he'll bring the recognition accuracy of his model above 99%. Content applies to software developers of all levels. Experienced machine learning enthusiasts, this video will introduce you to TensorFlow through well known models such as dense and convolutional networks. This is an intense technical video designed to help beginners in machine learning ramp up quickly.

We still need to boil the information down. In the last layer, we still want only 10 neurons for our 10 classes of digits. Traditionally, this was done by a "max-pooling" layer. Even if there are simpler ways today, "max-pooling" helps understand intuitively how convolutional networks operate: if you assume that during training, our little patches of weights evolve into filters that recognise basic shapes (horizontal and vertical lines, curves, ...) then one way of boiling useful information down is to keep through the layers the outputs where a shape was recognised with the maximum intensity. In practice, in a max-pool layer neuron outputs are processed in groups of 2x2 and only the one max one retained.

Google has recently open-sourced its framework for machine learning and neural networks called Tensorflow. 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 ofTensorflow Python, and a bag of "tricks of the trade" will do the job. No previous Python knowledge required.

Rich Sutton is old school king of RL.

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The most promising aspect of Deep Learning for me is the possibility of removing many of the "magic touch of the analyst" steps of feature extraction, model selection, manual data transformations, etc. that make machine learning models traditionally difficult to generalize. I tried to take a small step towards improvements of this process with my dissertation research by finding features that would generalize between scenes and using those to automatically classify urban areas in images. I used those cross-scene features to provide labels for spectra extracted through an unsupervised process. I'm struck by the fact that deep learning tasks in image recognition take this process back one step further and provide a means to learn those features at multiple orders and levels of abstraction. To see what I'm referring to, see some of the papers on deep convolutional networks, for example: