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Deep learning intro

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Non-techical introduction to deep learning.

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Everything you’ve always wanted to know about deep learning* (*but were afraid to ask**) (**in 15 minutes or less) Andrew Beam, PhD DBMI Postdoc happy hour 05/20/2016 e-mail: Andrew_Beam@hms.harvard.edu twitter: @AndrewLBeam
deep learning what? why? how?
what?
Neural Net History Slide credit: http://www.slideshare.net/deview/251-implementing-deep-learning-using-cu-dnn
Neural Networks Neural Networks are a flexible family of machine learning algorithms. • Not just one algorithm/model (MLP, RNN, CNN, etc) • Paired with (relatively) generic optimizers that “work” in a lot of scenarios • Trained using GPUs = big models on big data • Make it sound like you’re doing cool, cutting-edge neuroscience
Neural Nets Given data matrix (X), model response (y) compositionally: y = f3(f2(f1(X))) where fi() takes a weighted combination of inputs, followed by a nonlinear transformation For example: f1(x) = max(0,x*w)
Neural Nets Image credit: http://cs231n.github.io/neural-networks-1/
Parameter Learning Given data, find the “best” set of network parameter values. Learning done via the back propagation (“backprop”) algorithm
Convolutional Neural Networks aka CNNs/Convnets Convolution Operator Convnet Image credit: http://deeplearning.stanford.edu/wiki/index.php/Feature_extraction_using_convolution http://cs231n.github.io/convolutional-networks/
why?
they work!* - *for some problems - *when there is a lot of labeled data - *and the underlying function is complex but not noisy
The Breakthrough “AlexNet”
The Aftermath
Evolution of Deep Learning Architectures LeNet 1998
Evolution of Deep Learning Architectures LeNet 1998 AlexNet 2012
Evolution of Deep Learning Architectures LeNet 1998 AlexNet 2012 GoogLeNet 2014
Evolution of Deep Learning Architectures LeNet 1998 AlexNet 2012 GoogLeNet 2014 “Inception” 2015
Evolution of Deep Learning Architectures LeNet 1998 AlexNet 2012 GoogLeNet 2014 “Inception” 2015 ResNet 2016 56 layers!
Current State of the Art Reference: https://arxiv.org/abs/1603.09382
Deep Learning Everywhere Startups Fortune 500
Deep Learning in Genomics https://github.com/gokceneraslan/awesome-deepbio
how?
Deep Learning Frameworks High-level frameworks make deep learning easier Deep Learning Frameworks: • Keras • Lasagne • Caffe Graph compilers: • Theano • Tensor Flow Linear Algebra Libraries: • PyCuda (python) • CUDAMat (python) • JCuda (java)
Deep Learning (in 20 lines of python)
Resources References: • http://cs231n.github.io • http://cs224d.stanford.edu • http://colah.github.io • https://www.reddit.com/r/MachineLearning/ • http://www.andreykurenkov.com/writing/a-brief- history-of-neural-nets-and-deep-learning/ Software libraries • Keras (good place to start): http://keras.io • Lasagne: https://github.com/Lasagne/Lasagne • TensorFlow: https://www.tensorflow.org • Theano: http://deeplearning.net/software/theano/

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