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DEEP LEARNINGBreakthrough In Machine Learning

Intro & Tour

Central Florida Machine Learning, Predictive Analytics and Automated Reasoning

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INTROS

Exploring and sharing

How to…

What’s new / hot

Engineering Resources

Data Science

Big Data

Machine Learning

Semantics

Predictive Analytics

Internet of Things

Artificial Intelligence

Language Processing

DEEP LEARNINGBreakthrough In Machine Learning

Intro & Tour

Bill Gates, then Chairman, Microsoft

“A breakthrough in machine learning would

be worth ten Microsofts.”

Deep Learning Algos

• Deep Boltzmann Machine (DBM)

• Deep Belief Networks (DBN)

• Convolutional Neural Network (CNN)

• Stacked Auto-Encoders

HISTORY• Neural nets - big in the late 80’s -

Despite a commonly-held belief, there have been numerous successful applications

• Out of fashion in the 90’s

• 2003 renewed interest in the problem of learning representations (as opposed to just learning simple classifiers) - LeChun

• 2006-2007 traction via unsupervised training - Ng

• Now “Deep Learning” has come to designate any learning method that can train a system with more than 2 or 3 non-linear hidden layers.

WHY NOW?• More and diverse data

• More processing power

• Algorithm advances and discoveries

• GPUs

Andrew Ng Yann

LeCun

EXAMPLES

Image Recognition

Image Recognition

Speech Recognition

Natural Language

• Constituency parsing

• Sentiment analysis

• Information retrieval

• Machine translation

• Contextual entity linking

VIDEO

Andrew Ng: Deep Learning…

https://www.youtube.com/watch?v=n1ViNeWhC24

Introduction to Deep Learning with Python

https://www.youtube.com/watch?v=S75EdAcXHKk

TOOLS

TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community.

At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibility in implementing complex neural network topologies. You can build arbitrary graphs of neural networks, and parallelize them over CPUs and GPUs in an efficient manner.

• Pylearn2 is a machine learning library. Most of its functionality is built on top of Theano. It provides parallelization with CPUs and GPUs.

• Theano — An open source machine learning library for Python.• Deeplearning4j — An open source deep learning library written for Java. It provides parallelization with

CPUs and GPUs.• OpenNN — An open source C++ library which implements deep neural networks and provides

parallelization with CPUs.• NVIDIA cuDNN — A GPU-accelerated library of primitives for deep neural networks.• DeepLearnToolbox — A Matlab/Octave toolbox for deep learning.• convnetjs — A Javascript library for training deep learning models. It contains online demos.• Gensim — A toolkit for natural language processing implemented in the Python programming language.• Caffe — A deep learning framework.• Apache SINGA — A deep learning platform developed for scalability, usability and extensibility.• RNNLM — RNN language model open source.• RNNLMPara — Parallel RNN language model trainer open source.

Other Tools

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