Deploying Enterprise Deep Learning Masterclass Preview - Enterprise Deep Learning
AI Deep Learning - CF Machine Learning
-
Upload
karl-seiler -
Category
Software
-
view
152 -
download
0
Transcript of AI Deep Learning - CF Machine Learning
DEEP LEARNINGBreakthrough In Machine Learning
Intro & Tour
Central Florida Machine Learning, Predictive Analytics and Automated Reasoning
“Smart tech is here, now, and integral to your engineering.”
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
Karl Seiler | [email protected]@pivitguruSMARTER CHANGE