Downloaded 32 times
Uploaded byKarl Seiler
AI Deep Learning - CF Machine Learning
This document provides an introduction and overview of deep learning, including its history and key figures. Deep learning is a breakthrough in machine learning that uses neural networks with multiple hidden layers to learn representations of data. It has gained traction in recent years due to increases in data, processing power, and algorithmic advances. Popular deep learning algorithms and tools are described.
More Related Content
Similar to AI Deep Learning - CF Machine Learning
AI Deep Learning - CF Machine Learning
- 1. DEEP LEARNINGBreakthrough InMachine Learning Intro & Tour
- 2. Central Florida Machine Learning,Predictive Analytics and Automated Reasoning “Smart tech is here, now, and integral to your engineering.”
- 3.
- 4. Exploring and sharing Howto… What’s new / hot Engineering Resources
- 5. Data Science Big Data MachineLearning Semantics Predictive Analytics Internet of Things Artificial Intelligence Language Processing
- 6. DEEP LEARNINGBreakthrough InMachine Learning Intro & Tour
- 7. Bill Gates, thenChairman, Microsoft “A breakthrough in machine learning would be worth ten Microsofts.”
- 11. Deep Learning Algos •Deep Boltzmann Machine (DBM) • Deep Belief Networks (DBN) • Convolutional Neural Network (CNN) • Stacked Auto-Encoders
- 12. 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.
- 13. WHY NOW? • Moreand diverse data • More processing power • Algorithm advances and discoveries • GPUs
- 14.
- 15.
- 16.
- 17.
- 18.
- 19. Natural Language • Constituency parsing •Sentiment analysis • Information retrieval • Machine translation • Contextual entity linking
- 20.
- 21.
- 22. Introduction to DeepLearning with Python https://www.youtube.com/watch?v=S75EdAcXHKk
- 23.
- 24. TensorFlow™ is anopen 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.
- 25. The goal ofTorch 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.
- 26. • Pylearn2 isa 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
- 27. Karl Seiler |President karl@piviting.com Piviting.com @pivitguru SMARTER CHANGE