Deep learning is a machine learning technique that uses neural networks with multiple hidden layers between the input and output layers to model high-level abstractions in data. It can perform complex pattern recognition and feature extraction through multiple transformations of the input data. Deep learning techniques like deep neural networks, convolutional neural networks, and deep belief networks have achieved significant performance improvements in areas like computer vision, speech recognition, and natural language processing compared to traditional machine learning methods.
Deep Learning and Tensorflow Implementation(딥러닝, 텐서플로우, 파이썬, CNN)_Myungyon Ki...Myungyon Kim
Deep learning and Tensorflow implementation
2016.11.16
<Cotents>
Feature Engineering
Deep Neural Network
Tensorflow
Tensorflow Implementation
Future works
References
This slides deals with several things about deep learning.
ex) History of Deep learning, Several difficulties and breakthroughs. Things related to deep learning such as activation functions, perceptrons, Backpropagation, pre-train, drop-out, Convolutional Neural Network (CNN), Simple implementation of Tensor Flow, Python, and so on.
딥러닝, 기계학습, 머신러닝, 텐서플로우, 파이썬
Artificial neural network for machine learninggrinu
An Artificial Neurol Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human brain processes information. ANN includes a large number of connected processing units that work together to process information. They also generate meaningful results from it.
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
Deep Learning and Tensorflow Implementation(딥러닝, 텐서플로우, 파이썬, CNN)_Myungyon Ki...Myungyon Kim
Deep learning and Tensorflow implementation
2016.11.16
<Cotents>
Feature Engineering
Deep Neural Network
Tensorflow
Tensorflow Implementation
Future works
References
This slides deals with several things about deep learning.
ex) History of Deep learning, Several difficulties and breakthroughs. Things related to deep learning such as activation functions, perceptrons, Backpropagation, pre-train, drop-out, Convolutional Neural Network (CNN), Simple implementation of Tensor Flow, Python, and so on.
딥러닝, 기계학습, 머신러닝, 텐서플로우, 파이썬
Artificial neural network for machine learninggrinu
An Artificial Neurol Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human brain processes information. ANN includes a large number of connected processing units that work together to process information. They also generate meaningful results from it.
Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.
Fundamental, An Introduction to Neural NetworksNelson Piedra
An introduction to Neural Networks, eight edition, 1996
Authors: Ben Krose, Faculty of Mathematics & Computer Science, University of Amsterdam. Patrick wan der Smagt, Institute of Robotics and Systems Dynamics, German Aerospace Research Establishment
Keynote: Nelson Piedra, Computer Sciences School - Advanced Tech, Technical University of Loja UTPL, Ecuador.
Character Recognition using Artificial Neural NetworksJaison Sabu
Mini Project, Computer Science Department, College of Engineering Chengannur 2003-2007, Affiliated to Cochin University of Science and Technology (CUSAT), Kerala, India
This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks, followed by a TypeScript-based code sample that replicates the Tensorflow playground. Basic knowledge of matrices is helpful for this session.
Fundamental, An Introduction to Neural NetworksNelson Piedra
An introduction to Neural Networks, eight edition, 1996
Authors: Ben Krose, Faculty of Mathematics & Computer Science, University of Amsterdam. Patrick wan der Smagt, Institute of Robotics and Systems Dynamics, German Aerospace Research Establishment
Keynote: Nelson Piedra, Computer Sciences School - Advanced Tech, Technical University of Loja UTPL, Ecuador.
Character Recognition using Artificial Neural NetworksJaison Sabu
Mini Project, Computer Science Department, College of Engineering Chengannur 2003-2007, Affiliated to Cochin University of Science and Technology (CUSAT), Kerala, India
This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks, followed by a TypeScript-based code sample that replicates the Tensorflow playground. Basic knowledge of matrices is helpful for this session.
Gives an Introduction to Deep learning, What can you achieve with deep learning. What is deep learning's relationship with machine learning. Technical basics of working of deep learning. Introduction to LSTM. How LSTM can be used for Text classification. Results obtained.. Practical recommendations.
Introduction to Deep Learning (Dmytro Fishman Technology Stream) IT Arena
Lviv IT Arena is a conference specially designed for programmers, designers, developers, top managers, inverstors, entrepreneur and startuppers. Annually it takes place on 2-4 of October in Lviv at the Arena Lviv stadium. In 2015 conference gathered more than 1400 participants and over 100 speakers from companies like Facebook. FitBit, Mail.ru, HP, Epson and IBM. More details about conference at itarene.lviv.ua.
Introduction to Deep Learning with Will ConstableIntel Nervana
Deep Residual Nets, Activity recognition in videos, and Q&A systems using neon and the Nervana Cloud
Will Constable will start with an introduction to the field of Deep Learning, neon and the Nervana Cloud. The presentation will be followed by an interactive workshop using neon. neon is an open-source Python based Deep Learning framework that has been built from the ground up for speed, scalability and ease of use.
Introduction to deep learning @ Startup.ML by Andres RodriguezIntel Nervana
Deep learning is unlocking tremendous economic value across various market sectors. Individual data scientists can draw from several open source frameworks and basic hardware resources during the very initial investigative phases but quickly require significant hardware and software resources to build and deploy production models. Intel offers various software and hardware to support a diversity of workloads and user needs. Intel Nervana delivers a competitive deep learning platform to make it easy for data scientists to start from the iterative, investigatory phase and take models all the way to deployment. This platform is designed for speed and scale, and serves as a catalyst for all types of organizations to benefit from the full potential of deep learning. Example of supported applications include but not limited to automotive speech interfaces, image search, language translation, agricultural robotics and genomics, financial document summarization, and finding anomalies in IoT data.
Introduction to Recurrent Neural Network with Application to Sentiment Analys...Artifacia
This is the presentation from our first AI Meet held on Nov 19, 2016.
You can join Artifacia AI Meet Bangalore Group: https://www.meetup.com/Artifacia-AI-Meet/
Neural Networks in the Wild: Handwriting RecognitionJohn Liu
Demonstration of linear and neural network classification methods for the problem of offline handwriting recognition using the NIST SD19 Dataset. Tutorial on building neural networks in Pylearn2 without YAML. iPython notebook located at nbviewer.ipython.org/github/guard0g/HandwritingRecognition/tree/master/Handwriting%20Recognition%20Workbook.ipynb
Artificial Intelligence, Machine Learning and Deep LearningSujit Pal
Slides for talk Abhishek Sharma and I gave at the Gennovation tech talks (https://gennovationtalks.com/) at Genesis. The talk was part of outreach for the Deep Learning Enthusiasts meetup group at San Francisco. My part of the talk is covered from slides 19-34.
This presentation is Part 2 of my September Lisp NYC presentation on Reinforcement Learning and Artificial Neural Nets. We will continue from where we left off by covering Convolutional Neural Nets (CNN) and Recurrent Neural Nets (RNN) in depth.
Time permitting I also plan on having a few slides on each of the following topics:
1. Generative Adversarial Networks (GANs)
2. Differentiable Neural Computers (DNCs)
3. Deep Reinforcement Learning (DRL)
Some code examples will be provided in Clojure.
After a very brief recap of Part 1 (ANN & RL), we will jump right into CNN and their appropriateness for image recognition. We will start by covering the convolution operator. We will then explain feature maps and pooling operations and then explain the LeNet 5 architecture. The MNIST data will be used to illustrate a fully functioning CNN.
Next we cover Recurrent Neural Nets in depth and describe how they have been used in Natural Language Processing. We will explain why gated networks and LSTM are used in practice.
Please note that some exposure or familiarity with Gradient Descent and Backpropagation will be assumed. These are covered in the first part of the talk for which both video and slides are available online.
A lot of material will be drawn from the new Deep Learning book by Goodfellow & Bengio as well as Michael Nielsen's online book on Neural Networks and Deep Learning as well several other online resources.
Bio
Pierre de Lacaze has over 20 years industry experience with AI and Lisp based technologies. He holds a Bachelor of Science in Applied Mathematics and a Master’s Degree in Computer Science.
https://www.linkedin.com/in/pierre-de-lacaze-b11026b/
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
2. What is Deep Learning?
Deep learning is a set of algorithms in machine learning that attempt to
model high-level abstractions in data by using architectures composed of
multiple non-linear transformations.
• Multiple Layer Deep Neural Networks
• Work for Media and Unstructured Data
• Automatic Feature Engineering
• Complex Architectures and Computationally Intensive
7. Historical Background: First Generation ANN
• Perceptron (~1960) used a
layer of hand-coded
features and tried to
recognize objects by
learning how to weight
these features.
– There was a neat learning
algorithm for adjusting the
weights.
– But perceptron nodes are
fundamentally limited in
what they can learn to do.
Non-Adaptive
Hand Coded
Features
Output Class Labels
Input Feature
Sketch of a typical perceptron
from the 1960’s
Bomb Toy
8. Multiple Layer Perceptron ANN (1960~1985)
input vector
hidden
layers
outputs
Back-propagate
error signal to get
derivatives for
learning
Compare outputs with
correct answer to get
error signal
10. • It requires labeled training data.
Almost all data is unlabeled
• The learning time does not scale well
It is very slow in networks with
multiple hidden layers.
It can get stuck in poor local
optima.
Disadvantages
Back Propagation Algorithm
• Multi layer Perceptron network can be
trained by the back propagation
algorithm to perform any mapping
between the input and the output.
Advantages
11. Support Vector Machines
• Vapnik and his co-workers developed a very clever type of
perceptron called a Support Vector Machine.
o Instead of hand-coding the layer of non-adaptive features, each
training example is used to create a new feature using a fixed
recipe.
• The feature computes how similar a test example is to that
training example.
o Then a clever optimization technique is used to select the best
subset of the features and to decide how to weight each feature
when classifying a test case.
• But its just a perceptron and has all the same limitations.
• In the 1990’s, many researchers abandoned neural networks with
multiple adaptive hidden layers because Support Vector Machines
worked better.
26. Greedy Layer-Wise Training
• Train first layer using your data without the labels (unsupervised)
Since there are no targets at this level, labels don't help.
Could also use the more abundant unlabeled data which is
not part of the training set (i.e. self-taught learning).
• Then freeze the first layer parameters and start training the
second layer using the output of the first layer as the
unsupervised input to the second layer
• Repeat this for as many layers as desired
This builds our set of robust features
• Use the outputs of the final layer as inputs to a supervised
layer/model and train the last supervised layers(leave early
weights frozen)
• Unfreeze all weights and fine tune the full network by training
with a supervised approach, given the pre-processed weight
settings
28. Benefit of Greedy Layer-Wise Training
• Greedy layer-wise training avoids many of the problems of
trying to train a deep net in a supervised fashion
o Each layer gets full learning focus in its turn since it is the
only current "top" layer
o Can take advantage of unlabeled data
o When you finally tune the entire network with supervised
training the network weights have already been adjusted so
that you are in a good error basin and just need fine tuning.
This helps with problems of
• Ineffective early layer learning
• Deep network local minima
• Two most common approaches
o Stacked Auto-Encoders
o Deep Belief Networks
28
30. What Auto-Encoder Can Do?
• A type of unsupervised learning which tries to discover generic features of
the data
o Learn identity function by learning important sub-features not by just
passing through data
o Can use just new features in the new training set or concatenate both
34. Stacked Auto-Encoders Approach
• Stack many sparse auto-encoders in succession and train them using greedy
layer-wise training
• Drop the decode output layer each time
• Do supervised training on the last layer using final features
• Finally do supervised training on the entire network to fine- tune all weights
35. What is Sparse Encoders?
• Auto encoders will often do a dimensionality reduction
o PCA-like or non-linear dimensionality reduction
• This leads to a "dense" representation which is nice in terms of
parsimony
o All features typically have non-zero values for any input and the
combination of values contains the compressed information
• However, this distributed and entangled representation can often
make it more difficult for successive layers to pick out the salient
features
• A sparse representation uses more features where at any given time
a significant number of the features will have a 0 value
o This leads to more localist variable length encodings where a
particular node (or small group of nodes) with value 1 signifies the
presence of a feature (small set of bases)
o A type of simplicity bottleneck (regularizer)
o This is easier for subsequent layers to use for learning
36. Implementation of Sparse Auto-Encoder
• Use more hidden nodes in the encoder
• Use regularization techniques which encourage
sparseness e.g. a significant portion of nodes have 0
output for any given input
o Penalty in the learning function for non-zero nodes
with weight decay
• De-noising Auto-Encoder
o Stochastically corrupt training instance each time, but
still train auto-encoder to decode the uncorrupted
instance, forcing it to learn conditional dependencies
within the instance
o Better empirical results, handles missing values well
37. General Belief Nets
• A belief net is a directed
acyclic graph composed of
stochastic variables.
• Solve two problems:
The inference problem:
Infer the states of the
unobserved variables.
The learning problem:
Adjust the interactions
between variables to make
the network more likely to
generate the observed data.
stochastic
hidden
cause
visible
effect
Use nets composed of layers of
stochastic binary variables with
weighted connections. Other types of
variable can be generalized as well.
38. Stochastic Binary Units
(Bernoulli Variables)
• Variables with state of 1
or 0;
• The probability of turning
on is determined by the
weighted input from
other units (plus a bias)
0
0
1
j
jiji
i
wsb
sp
)exp(1
)(
1
1
j
jiji wsb
)( 1isp
39. Learning Rule for Sigmoid Belief Nets
• Learning is easy if we can get
an unbiased sample from the
posterior distribution over
hidden states given the
observed data.
• For each unit, maximize the
log probability that its binary
state in the sample from the
posterior would be
generated by the sampled
binary states of its parents.
j
jij
ii
ws
spp
)exp(1
)(
1
1
j
i
jiw
)( iijji pssw
is
js
learning
rate
40. Problems with Deep Belief Nets
Since DBNs are directed graph model, given input data, the posterior of
hidden units is intractable due to the “explaining away” effect. Even two
hidden causes are independent, they can become dependent when we
observe an effect that they can both influence.
Solution: Complementary Priors to ensure the posterior of hidden units
are under the independent constraints.
truck hits house earthquake
house
jumps
20 20
-20
-10 -10
General Deep Belief Nets
Explaining Away Effect
p(1,1)=.0001
p(1,0)=.4999
p(0,1)=.4999
p(0,0)=.0001
posterior
41. Complementary Priors
Definition of Complementary Priors:
Consider observations x and hidden variables y, for a given likelihood function P(x|y), the
priors of y, P(y) is called the complementary priors of P(x|y), provided that P(x,y)=P(x|y)
P(y) leads to the posteriors P(y|x) .
Infinite directed model with tied weights and Complementary Priors and
Gibbs sampling:
Recall that the RBMs have the property
The definition of energy function of RBM makes it proper model that has
two sets of conditional independencies(complementary priors for both v
and h)
Since we need to estimate the distribution of data, P(v), we can perform
Gibbs sampling alternatively from P(v,h) for infinite times. This procedure
is analogous to unroll the single RBM into infinite directed stacks of
RBMs with tied weights(due to “complementary priors”) where each
RBM takes input from the hidden layer of the lower level RBM.
n
j
j
m
i
i
vhPP
hvPP
1
1
)|()v|h(
)|(h)|(v
42. Restricted Boltzmann Machines
• Restrict the connectivity to make
learning easier.
Only one layer of hidden units
No connections between hidden units.
• The hidden units are conditionally
independent given the visible states.
Quickly get an unbiased sample from
the posterior distribution when given a
data-vector, which is a big advantage
over directed belief nets
hidden
i
j
visible
43. Energy of A Joint Configuration
ji
ijji whvv,hE
,
)(
weight
between units i
and j
Energy with configuration v
on the visible units and h
on the hidden units
binary state of
visible unit i
binary state of
hidden unit j
ji
ij
hv
w
hvE
),(
44. Weights, Energies and Probabilities
• Each possible joint configuration of the visible and hidden
units has an energy
The energy is determined by the weights and biases as in
a Hopfield net.
• The energy of a joint configuration of the visible and hidden
units determines its probability:
• The probability of a configuration over the visible units is
found by summing the probabilities of all the joint
configurations that contain it.
),(
),(
hvE
hvp e
45. Using Energies to Define Probabilities
• The probability of a joint
configuration over both visible
and hidden units depends on
the energy of that joint
configuration compared with
the energy of all other joint
configurations.
• The probability of a
configuration of the visible
units is the sum of the
probabilities of all the joint
configurations that contain it.
gu
guE
hvE
e
e
hvp
,
),(
),(
),(
gu
guE
h
hvE
e
e
vp
,
),(
),(
)(
partition
function
46. Maximum Likelihood RBM Learning Algorithm
0
jihv
jihv
i
j
i
j
i
j
i
j
t = 0 t = 1 t = 2 t = infinity
jiji
ij
hvhv
w
vp 0)(log
Start with a training vector on the visible units.
Then alternate between updating all the hidden units in
parallel and updating all the visible units in parallel.
a fantasy
47. A Quick Way to Learn an RBM
0
jihv 1
jihv
i
j
i
j
t = 0 t = 1
)( 10
jijiij hvhvw
• Start with a training vector on
the visible units.
• Update all the hidden units in
parallel
• Update the all the visible units
in parallel to get a
“reconstruction”.
• Update the hidden units again.
Contrastive divergence: This is not following the gradient of the
log likelihood. But it works well. It is approximately following the
gradient of another objective function.
reconstructiondata