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• Deep Learning
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Computer vision - Related fields
1
neural net and deep learning based image
and feature analysis and classification)
have their background in biology.
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Machine learning - Representation learning
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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
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Artificial neural network - History
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.
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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-inspireddeep-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
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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
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Andrew Ng
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.
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Andrew Ng - Machine learning research
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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.
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Ben Goertzel - Papers
* 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
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Ben Goertzel - Papers
* 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_A
GI_11.pdf]
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Serbo-Croatian - Croatian linguists
: 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.
1
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Pattern recognition - Regression analysis|Regression algorithms (predicting real
number|real-valued labels)
1
*Neural networks and
Deep learning|Deep
learning methods
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Deep learning
'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.
1
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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.
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Deep learning
1
Ronan Collobert has said that deep learning is
just a buzzword for neural nets
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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.ed
u/~hinton/absps/tics.pdf Learning
multiple layers of representation]
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Deep learning - Introduction
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
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Deep learning - Introduction
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
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Deep learning - Introduction
1
and has attracted the attention of such
thinkers as Ray Kurzweil, who was
hired by Google to do deep learning
research.
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Deep learning - Introduction
1
Gary Marcus has expressed skepticism of deep
learning's capabilities, noting that
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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.
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Deep learning - Fundamental concepts
Deep learning algorithms often
involve other important ideas that
correspond to broad a priori beliefs
about these unknown underlying
factors
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Deep learning - Fundamental concepts
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.
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Deep learning - Deep learning in artificial neural networks
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Deep Learning Neural Networks date back at least
to the 1980 Neocognitron by Kunihiko Fukushima
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Deep learning - Deep learning in artificial neural networks
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
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Deep learning - Deep learning in artificial neural networks
1
As of 2011, the state of the art in deep
learning feedforward networks alternates
convolutional layers and max-pooling
layers,D
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Deep learning - Deep learning in artificial neural networks
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. Multicolumn Deep Neural Networks for Image
Classification. IEEE Conf. on Computer
Vision and Pattern Recognition CVPR
2012.
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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 selforganization 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
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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
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Handwriting recognition - Results since 2009
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
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Mind uploading in fiction - Literature
* 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
1
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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.
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Long short term memory - Applications
*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. 2011
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Jürgen Schmidhuber
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/ho
w-bio-inspired-deep-learning-keepswinning-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
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John F. Kennedy University - Service to Community
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.
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Waldorf education - Educational scholars
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 people
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Camille Paglia - Feminism
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.
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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
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Aleph (psychedelic) - Aleph-4
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Effects: profound and deep
learning experiences Alexander Shulgin
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Pierre Baldi - Career
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.
1
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Blissymbols - Semantics
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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.
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Torch (machine learning)
1
'Torch' is an open source deep learning
library for the Lua (programming
language)|Lua programming language
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Torch (machine learning) - Applications
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the Facebook AI Research Group,[http://www.kdnuggets.com/2014/02/exclusiveyann-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?comm
ent_id=10152089275552143offset=0total_comments=6 Yann Lecun's FaceBook
Page] and the Idiap Research Institute.[https://www.idiap.ch/scientificresearch/resources/torch IDIAP Research Institute : Torch] It is used and cited in
240 research
papers.[http://scholar.google.ca/scholar?cites=9993075313749753697as_sdt=2005
sciodt=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=20
05sciodt=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 Torchios 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]
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Restricted Boltzmann machine
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.
1
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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.
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Socratic questioning - Pedagogy
1
It teaches us the value of developing questioning
minds in cultivating deep learning
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Google Brain
1
'Google Brain' is an unofficial name for a deep
learning research project at Google.
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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
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Google Brain - History
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.
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Google Brain - History
1
Moreover, In December 2012, futurist and
inventor Ray Kurzweil, author of The
Singularity is Near, joined Google in a fulltime 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.
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Image classification - Related fields
Neural network|neural net and deep
learning based image and feature analysis
and classification) have their background
in biology.
1
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Charles du Fresne, sieur du Cange - Charles Du Fresne
His great historical and linguistic
knowledge was complemented by equally
deep learning in archaeology, geography
and law
1
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Robert Byrd - Reaction to death
*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.
1
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Learning representation
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
1
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Geoffrey Hinton
He is the co-inventor of the
backpropagation and contrastive
divergence training algorithms and is an
important figure in the deep learning
movement.
1
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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
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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/howbio-inspired-deep-learning-keeps-winningcompetitions 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)
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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.
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List of text mining software - Commercial
1
* Semantria - offers its services via API
and Excel plugin. It is a spinoff of textanalysis 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.
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Giosuè Carducci
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.
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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.
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Sepp Hochreiter - Deep Neural Networks and Recurrent Neural Networks
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.
1
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Deep belief network
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.
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Deep belief network
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.
1
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List of artificial intelligence projects - Software packages
* 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.
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Learning styles - A more recent evidence-based model of learning
A high drive to explore leads to
dysfunctional learning consequences
unless cognitions such as goal
orientation, conscientiousness, deep
learning and emotional intelligence reexpress it in more complex ways to
achieve functional outcomes such as
high work performance
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Eugen Weber
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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.
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Adrienne Rich - Later life: 1976–2012
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
1
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