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Home > Machine Learning Guide > 9. Deep Learning
Podcast: Machine Learning Guide
Episode:

9. Deep Learning
Category: Technology
Duration: 00:51:09
Publish Date: 2017-03-03 23:00:00
Description:

Deep learning and neural networks. How to stack our logisitic regression units into a multi-layer perceptron.

## Resources - Overview: ** Deep Learning Simplified (https://www.youtube.com/watch?v=b99UVkWzYTQ) `video:easy` quick series to get a lay-of-the-land. - Quickstart: ** TensorFlow Tutorials (https://www.tensorflow.org/get_started/get_started) `tutorial:medium` - Deep-dive code (pick one): ** Fast.ai (http://course.fast.ai/) `course:medium` practical DL for coders ** Hands-On Machine Learning with Scikit-Learn and TensorFlow (http://amzn.to/2tVdIXN) `book:medium` - Deep-dive theory (pick one): ** Deep Learning Book (http://amzn.to/2tXgCiT) (Free HTML version (http://www.deeplearningbook.org/)) `book:hard` comprehensive DL bible; highly mathematical ** Neural Networks and Deep Learning (http://neuralnetworksanddeeplearning.com/) `book:medium` shorter online "book"

## Episode - Value ** Represents brain? Magic black-box ** Feature learning (layer removed from programmer) ** Subsumes AI - Stacked shallow learning ** Logistic regression = lego, Neural Network = castle - Deep Learning => ANNs => MLPs (& RNNs, CNNs, DQNs, etc) ** MLP: Perceptron vs LogReg / sigmoid activation - Architecture ** (Feed forward) Input => Hidden Layers => Hypothesis fn ** "Feed forward" vs recursive (RNNs, later) ** (Loss function) Cross entropy ** (Learn) Back Propagation - Price ~ smoking + obesity + age^2 ** 1-layer MLP - Face? ~ pixels ** Extra layer = hierarchical breakdown ** Inputs => Employees => Supervisors => Boss - Backprop / Gradient descent ** Optimizers: adagrad, adam, ... vs gradient descent - Silver bullet, but don't abuse ** linear (housing market) ** features don't combine ** expensive: like hiring a company when the boss h(x) does all the work - Brian comparison (dentrites, axons); early pioneers as neuroscientists / cogsci - Different types ** vs brain ** RNNs ** CNNs - Activation fns ** Activation units / neurons (hidden layer) ** Relu, TanH, Sigmoid
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