deep learning

• Deep Learning

https://store.theartofservice.com/the-deep-learning-toolkit.html

Computer vision - Related fields

1 neural net and deep learning based image and feature analysis and

classification) have their background in biology.


Machine learning - Representation learning

1 Deep learning algorithms discover multiple levels of representation, or a

hierarchy of features, with higher-level, more abstract features defined

in terms of (or generating) lower-level features


Artificial neural network - History

1 In the 1990s, neural networks were overtaken in popularity in machine

learning by support vector machines and other, much simpler methods such as linear classifiers. Renewed

interest in neural nets was sparked in the 2000s by the advent of deep

learning.


Artificial neural network - Recent improvements

1 Between 2009 and 2012, the recurrent neural networks and deep feedforward neural networks developed in the research group of Jürgen Schmidhuber at the IDSIA|Swiss AI Lab IDSIA have won eight international competitions in

pattern recognition and machine learning.http://www.kurzweilai.net/how-bio-inspired-deep-

learning-keeps-winning-competitions 2012 Kurzweil AI Interview with Jürgen Schmidhuber on the eight

competitions won by his Deep Learning team 2009–2012 For example, multi-dimensional long short term memory

(LSTM)Graves, Alex; and Schmidhuber, Jürgen; Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks, in Bengio, Yoshua; Schuurmans, Dale;

Lafferty, John; Williams, Chris K


Artificial neural network - Recent improvements

1 Deep learning feedforward networks, such as convolutional neural

networks, alternate convolutional layers and max-pooling layers,

topped by several pure Statistical classification|classification layers


Andrew Ng

1 He researches primarily in Artificial Intelligence, machine learning, and deep

learning. His early work includes the Stanford Autonomous Helicopter project, which developed one of the most capable autonomous helicopters in the world, and the STAIR (STanford Artificial Intelligence

Robot) project, which resulted in ROS (Robot Operating System)|ROS, a widely used open

source software|open-source robotics software platform.


Andrew Ng - Machine learning research

1 Among its notable results was a neural network trained using deep learning algorithms on 16,000 CPU

cores, that learned to recognize higher-level concepts, such as cats, after watching only YouTube videos, and without ever having been told

what a cat is.


Ben Goertzel - Papers

1 * Goertzel, Ben (2011). Integrating a Compositional Spatiotemporal Deep

Learning Network with Symbolic Representation/Reasoning within an

Integrative Cognitive Architecture via an Intermediary Semantic Network. Proceedings of AAAI Symposium on

Cognitive Systems, Arlington VA


Ben Goertzel - Papers

1 * Goertzel, Ben (2011). Imprecise Probability as a Linking Mechanism Between Deep Learning, Symbolic

Cognition and Local Feature Detection in Vision Processing.

Proceedings of AGI-11, Lecture Notes in AI, Springer Verlag [

http://goertzel.org/VisualAttention_AGI_11.pdf]


Serbo-Croatian - Croatian linguists

1 : At the end of the 15th century [in Dubrovnik and Dalmatia], sermons and poems were exquisitely crafted in the Croatian language by those

men whose names are widely renowned by deep learning and piety.


Pattern recognition - Regression analysis|Regression algorithms (predicting real number|real-valued labels)

1 *Neural networks and Deep learning|

Deep learning methods


Deep learning

1 'Deep learning' is a set of algorithms in machine learning that attempt to learn in

multiple levels of representation, corresponding to different levels of

abstraction. It typically uses artificial neural networks. The levels in these learned

statistical models correspond to distinct levels of concepts, where higher-level

concepts are defined from lower-level ones, and the same lower-level concepts can help

to define many higher-level concepts.


Deep learning

1 Deep learning is part of a broader family of machine learning methods based on

learning representations. An observation (e.g., an image) can be represented in

many ways (e.g., a vector of pixels), but some representations make it easier to learn tasks of interest (e.g., is this the

image of a human face?) from examples, and research in this area attempts to define what makes better representations and how

to learn them.https://store.theartofservice.com/the-deep-learning-toolkit.html

Deep learning

1 Ronan Collobert has said that deep learning is just a buzzword for neural nets


Deep learning - Introduction

1 The term deep learning gained traction in the mid-2000s after a

publication by Geoffrey Hinton and Ruslan

Salakhutdinov[http://www.cs.toronto.edu/~hinton/absps/tics.pdf Learning

multiple layers of representation]



1 In 1992, Jürgen Schmidhuber had already implemented a very similar idea for the more

general case of unsupervised deep hierarchies of recurrent neural networks, and also experimentally

shown its benefits for speeding up supervised learning.Jürgen Schmidhuber|Schmidhuber, Jürgen; Learning complex, extended sequences using the

principle of history compression., Neural Computation, 4(2):234-242, 1992Jürgen

Schmidhuber|Schmidhuber, Jürgen; My First Deep Learning System of 1991 + Deep Learning Timeline

1962-2013, http://www.idsia.ch/~juergen/firstdeeplearner.html



1 Advances in hardware have been an important enabling factor for the

resurgence of neural networks and the advent of deep learning, in

particular the availability of powerful and inexpensive graphics processing

units (GPUs) also suitable for general-purpose computing on

graphics processing units|general-purpose computing



1 and has attracted the attention of such thinkers as Ray Kurzweil, who

was hired by Google to do deep learning research.



1 Gary Marcus has expressed skepticism of deep learning's capabilities, noting that


Deep learning - Fundamental concepts

1 The appropriate number of levels and the structure that relates these factors is something that a deep

learning algorithm is also expected to discover from examples.



1 Deep learning algorithms often involve other important ideas that correspond to broad a priori beliefs about these unknown underlying

factors



1 Many deep learning algorithms are actually framed as unsupervised learning, e.g., using

many examples of natural images to discover good representations of them.

Because most of these learning algorithms can be applied to unlabeled data, they can leverage large amounts of unlabeled data,

even when these examples are not necessarily labeled, and even when the data

cannot be associated with labels of the immediate tasks of interest.


Deep learning - Deep learning in artificial neural networks

1 Deep Learning Neural Networks date back at least to the 1980 Neocognitron by Kunihiko

Fukushima



1 Another method is the long short term memory (LSTM) network of 1997 by Sepp Hochreiter|Hochreiter Jürgen Schmidhuber|Schmidhuber.Sepp Hochreiter|Hochreiter,

Sepp; and Jürgen Schmidhuber|Schmidhuber, Jürgen; Long Short-Term Memory, Neural Computation, 9(8):1735–1780,

1997 In 2009, deep multidimensional LSTM networks demonstrated the power of deep learning with many

nonlinear layers, by winning three ICDAR 2009 competitions in connected handwriting recognition,

without any prior knowledge about the three different languages to be learned.Graves, Alex; and Schmidhuber,

Jürgen; Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks, in Bengio,

Yoshua; Schuurmans, Dale; Lafferty, John; Williams, Chris K



1 As of 2011, the state of the art in deep learning feedforward networks alternates convolutional layers and

max-pooling layers,D



1 Such supervised deep learning methods also were the first artificial

pattern recognizers to achieve human-competitive performance on certain tasks.D. C. Ciresan, U. Meier, J. Schmidhuber. Multi-column Deep

Neural Networks for Image Classification. IEEE Conf. on Computer Vision and Pattern

Recognition CVPR 2012.https://store.theartofservice.com/the-deep-learning-toolkit.html

Deep learning - Deep learning in the human brain

1 These models share the interesting property that various proposed learning dynamics in the brain

(e.g., a wave of neurotrophic growth factor) conspire to support the self-organization of just the sort of inter-related neural networks utilized in the later, purely computational deep learning models, and

which appear to be analogous to one way of understanding the neocortex of the brain as a

hierarchy of filters where each layer captures some of the information in the operating environment,

and then passes the remainder, as well as modified base signal, to other layers further up the hierarchy


Deep learning - Deep learning in the human brain

1 The theory of deep learning therefore sees the coevolution of culture and

cognition as a fundamental condition of human evolution.Shrager, J.,

Johnson, M


Handwriting recognition - Results since 2009

1 Recent GPU-based deep learning methods for feedforward networks by Dan Ciresan and colleagues at IDSIA won the ICDAR 2011 offline Chinese

handwriting recognition contest; their neural networks also were the first

artificial pattern recognizers to achieve human-competitive

performanceD


Mind uploading in fiction - Literature

1 * Clyde Dsouza's Memories with Maya (2013) looks at how deep learning processes, and 'Digital

Breadcrumbs' left behind by people (tweets, Facebook updates, blogs) combined with memories of living

relatives can be used to re-construct a mind and augment it with narrow AI libraries. The resulting 'Dirrogate' or Digital Surrogate can be thought of as a posthumous mind upload.


Multi-layer perceptron - Applications

1 but have since the 1990s faced strong competition from the much

simpler (and relatedR. Collobert and S. Bengio (2004). Links between

Perceptrons, MLPs and SVMs. Proc. Int'l Conf. on Machine Learning

(ICML).) support vector machines. More recently, there has been some renewed interest in backpropagation

networks due to the successes of deep learning.


Long short term memory - Applications

1 *Human action recognitionM. Baccouche, F. Mamalet, C Wolf, C.

Garcia, A. Baskurt. Sequential Deep Learning for Human Action

Recognition. 2nd International Workshop on Human Behavior

Understanding (HBU), A.A. Salah, B. Lepri ed. Amsterdam, Netherlands.

pp. 29–39. Lecture Notes in Computer Science 7065. Springer.

2011https://store.theartofservice.com/the-deep-learning-toolkit.html

Jürgen Schmidhuber

1 Between 2009 and 2012, the recurrent neural networks and deep feedforward neural networks developed in his research group have won eight international competitions in pattern recognition

and machine learning.[http://www.kurzweilai.net/how-bio-

inspired-deep-learning-keeps-winning-competitions 2012 Kurzweil AI Interview] with Jürgen Schmidhuber on the eight competitions won by his Deep Learning team 2009-2012 In

honor of his achievements he was elected to the European Academy of Sciences and Arts in 2008.


John F. Kennedy University - Service to Community

1 In keeping with its namesake, John F. Kennedy University is committed to

being of service to the local communities. As part of this

commitment and in recognition of the fact that deep learning often

happens outside the classroom, JFK University offers students the opportunity to gain practical

experience through the following clinical and internship opportunities.


Waldorf education - Educational scholars

1 This enables deep learning that goes beyond studying for the next

test.Fanny Jiménez, Wissenschaftler loben Waldorfschulen, Die Welt, 27 September 2012 Deborah Meier,

principal of Mission Hill School and MacArthur grant recipient, whilst having some quibbles about the

Waldorf schools, stated: The adults I know who have come out of Waldorf

schools are extraordinary peoplehttps://store.theartofservice.com/the-deep-learning-toolkit.html

Camille Paglia - Feminism

1 its deep learning and massive argument are unsurpassed) as well as Germaine Greer, but Time

magazine critic Martha Duffy wrote that Paglia does not hesitate to hurl brazen insults at several feminists

including Greer, whom Paglia accused of becoming a drone in three years as a result of her early success;

Paglia also called activist Diana Fuss' output just junk – appalling! Showalter calls Paglia unique in the

hyperbole and virulence of her hostility to virtually all the prominent feminist activists, public figures, writers

and scholars of her generation, mentioning Carolyn Heilbrun, Judith Butler, Carol Gilligan, Marilyn French, Zoe Baird, Kimba Wood, Susan Thomases, and Hillary

Clinton as targets of her criticism.https://store.theartofservice.com/the-deep-learning-toolkit.html

Pope Benedict XIV - Ascension to the papacy

1 This appears to have assisted his cause for winning the election, which also benefited from his reputation for

deep learning, gentleness, pomp, wisdom, and piety in policy


Aleph (psychedelic) - Aleph-4

1 Effects: profound and deep learning experiences -

Alexander Shulgin


Pierre Baldi - Career

1 Pierre Baldi's research include artificial intelligence, statistical

machine learning, and data mining, and their applications to problems in

the life sciences in genomics, proteomics, systems biology,

computational neuroscience, and, recently, deep learning.


Blissymbols - Semantics

1 Bliss’s concern about semantics finds an early referent in John Locke,Locke,

J. (1690). An Essay Concerning Human Understanding. London. whose Essay Concerning Human Understanding prevented people

from those vague and insignificant forms of speech that may give the impression of being deep learning.


Torch (machine learning)

1 'Torch' is an open source deep learning library for the Lua

(programming language)|Lua programming language


Torch (machine learning) - Applications

1 the Facebook AI Research Group,[http://www.kdnuggets.com/2014/02/exclusive-yann-lecun-deep-learning-facebook-ai-lab.html KDnuggets Interview with Yann LeCun, Deep Learning Expert, Director of Facebook AI Lab] the Computational Intelligence, Learning, Vision, and

Robotics Lab at NYU,[http://cilvr.nyu.edu/doku.php?id=code:start CILVR Lab Software] MADBITS,[http://code.madbits.com/wiki/doku.php Machine Learning with Torch7] IBM,

[https://news.ycombinator.com/item?id=7928738 Hacker News] Yandex[https://www.facebook.com/yann.lecun/posts/10152077631217143?

comment_id=10152089275552143offset=0total_comments=6 Yann Lecun's FaceBook Page] and the Idiap Research

Institute.[https://www.idiap.ch/scientific-research/resources/torch IDIAP Research Institute : Torch] It is used and cited in 240 research papers.[http://scholar.google.ca/scholar?

cites=9993075313749753697as_sdt=2005sciodt=0,5hl=en Google Scholar results for Torch: a modular machine learning software library citations] For comparison, Theano (software)|Theano, a similar library written in Python (programming language), C and

CUDA, has 138 citations.[http://scholar.google.ca/scholar?cites=8194189194999260817as_sdt=2005sciodt=0,5hl=en Theano: a CPU and GPU math

expression compiler] Torch has been extended for use on Android (operating system)|Android[https://github.com/soumith/torch-android Torch-android GitHub repository] and iOS.[https://github.com/clementfarabet/torch-ios Torch-ios GitHub repository] It has been used

to build hardware implementations for data flows like those found in neural networks.[http://pub.clement.farabet.net/ecvw11.pdf NeuFlow: A Runtime Reconfigurable Dataflow

Processor for Vision]


Restricted Boltzmann machine

1 Restricted Boltzmann machines can also be used in deep learning

networks. In particular, deep belief networks can be formed by stacking RBMs and optionally fine-tuning the resulting deep network with gradient

descent and backpropagation.


Tensor Network Theory - Neural Networks and Artificial Intelligence

1 Other applications include teaching computers how to recognize

handwriting, speech, and traffic signs by using deep learning which utilizes

artificial neural networks.


Socratic questioning - Pedagogy

1 It teaches us the value of developing questioning minds in cultivating deep learning


Google Brain

1 'Google Brain' is an unofficial name for a deep learning research project at Google.


Google Brain - History

1 Stanford University professor Andrew Ng who, since around 2006, became

interested in using deep learning techniques to crack the problem of

artificial intelligence, started Google's Deep Learning project

(which would later acquire the name Google Brain) in 2011 as one of the Google X projects. The project's first in-depth coverage was in the New

York Times in November 2011.https://store.theartofservice.com/the-deep-learning-toolkit.html


1 In March 2013, Google hired Geoffrey Hinton, a leading researcher in the

deep learning field, and acquired the company DNNResearch Inc. headed by Hinton. Hinton said that he would be dividing his future time between his university research and his work

at Google.



1 Moreover, In December 2012, futurist and inventor Ray Kurzweil, author of

The Singularity is Near, joined Google in a full-time engineering director

role, but focusing on the deep learning project. It was reported that

Kurzweil would have unlimited resources to pursue his vision at

Google. However, he is leading his own team, which is independent of

Google Brain.https://store.theartofservice.com/the-deep-learning-toolkit.html

Image classification - Related fields

1 Neural network|neural net and deep learning based image and feature

analysis and classification) have their background in biology.


Charles du Fresne, sieur du Cange - Charles Du Fresne

1 His great historical and linguistic knowledge was complemented by

equally deep learning in archaeology, geography and law


Robert Byrd - Reaction to death

1 *Party leaders of the United States Senate|Senate Republican leader

Mitch McConnell: Senator Byrd combined a devotion to the U.S.

Constitution with a deep learning of history to defend the interests of his

state and the traditions of the Senate. We will remember him for his fighter's spirit, his abiding faith, and for the many times he recalled the

Senate to its purposes.https://store.theartofservice.com/the-deep-learning-toolkit.html

Learning representation

1 Multilayer neural networks can also be considered to perform feature

learning, since they learn a representation of their input at the

hidden layer(s) which is subsequently used for classification or regression

at the output layer, and feature learning is an integral part of deep

learning, to the point that the two are sometimes considered synonyms


Geoffrey Hinton

1 He is the co-inventor of the backpropagation and contrastive

divergence training algorithms and is an important figure in the deep

learning movement.


List of Carnegie Mellon University people - Other prominent faculty

1 *Geoffrey Hinton (Professor, 1982-1987), computer scientist best

known for his work on artificial neural networks, part-time Google

researcher, co-inventor of the backpropagation and contrastive

divergence training algorithms, and is an important figure in the deep

learning movement


Distributed representation - Successes in pattern recognition contests since 2009

1 Between 2009 and 2012, the recurrent neural networks and deep feedforward neural networks

developed in the research group of Jürgen Schmidhuber at the IDSIA|Swiss AI Lab IDSIA have

won eight international competitions in pattern recognition and machine

learning.[http://www.kurzweilai.net/how-bio-inspired-deep-learning-keeps-winning-competitions 2012

Kurzweil AI Interview] with Jürgen Schmidhuber on the eight competitions won by his Deep Learning

team 2009–2012 For example, the bi-directional and multi-dimensional long short term memory (LSTM)


Deeplearning4j

1 'Deeplearning4j' is an open source deep learning library for

Java_(programming_language)|Java and the JVM|Java Virtual Machine and a computing

framework with wide support for deep learning algorithms. Deeplearning4j includes implementations of the restricted Boltzmann machine, deep belief net, deep autoencoder, stacked denoising autoencoder and recursive

neural network#Recursive Neural Tensor Network|recursive neural tensor network.


List of text mining software - Commercial

1 * Semantria - offers its services via API and Excel plugin. It is a spinoff of text-analysis software Lexalytics, but differs in that it is offered via API and

Excel plugin, and in that it incorporates a bigger knowledge

base and uses deep learning.


Giosuè Carducci

1 In 1906 he became the first Italian to receive the Nobel Prize in Literature not only in consideration of his deep

learning and critical research, but above all as a tribute to the creative energy, freshness of style, and lyrical

force which characterize his poetic masterpieces.


Sepp Hochreiter - Learning Representations and Low Complexity Neural Networks

1 means a low complex network that avoids overfitting. Low complexity neural networks are well suited for deep learning because they control

the complexity in each network layer and, therefore, learn learning representation|hierarchical

representations of the input.


Sepp Hochreiter - Deep Neural Networks and Recurrent Neural Networks

1 Recurrent neural networks scan and process sequences and supply their

results to the environment. Sepp Hochreiter developed the long short term memory, which overcomes the problem of previous recurrent and deep learning|deep networks to forget information over time or,

equivalently, through layers.


Deep belief network

1 In machine learning, a 'deep belief network' ('DBN') is a generative

model|generative graphical model, or alternatively a type of deep learning|deep artificial neural network|neural network, composed of multiple layers

of latent variables (hidden units), with connections between the layers

but not between units within each layer.


Deep belief network

1 The observation, due to Geoffrey Hinton|Hinton's student Teh, that

DBNs can be trained greedy algorithm|greedily, one layer at a

time, has been called a breakthrough in deep learning.


List of artificial intelligence projects - Software packages

1 * Deeplearning4j, an open-source, distributed deep learning framework written for the JVM. It integrates with

Hadoop/YARN and GPUs, and also spins up a standalone distributed

system. DL4J implements deep-belief nets, deep autoencoders,

convolutional nets, word2vec and recursive neural tensor networks.


Learning styles - A more recent evidence-based model of learning

1 A high drive to explore leads to dysfunctional learning consequences

unless cognitions such as goal orientation, conscientiousness, deep learning and emotional intelligence re-express it in more complex ways to achieve functional outcomes such

as high work performance


Eugen Weber

1 His 1,300-page Modern History of Europe: Men, Cultures, and Societies from the

Renaissance to the Present (1971) was described a phenomenal job of synthesis and interpretation that reflects Eugen's wide and deep learning, by his UCLA history colleague Hans Rogger.UCLA, In Memoriam In addition to his distinguished American Awards and

honors, he was awarded the Ordre des Palmes Académiques in 1977 for his

contribution to French culture.


Adrienne Rich - Later life: 1976–2012

1 She was the winner of the 2003 Yale Bollingen Prize for American Poetry

and applauded by the panel of judges for her honesty at once ferocious, humane, her deep

learning, and her continuous poetic exploration and awareness of

multiple selves


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