Brian Mac Namee - Predict Webinar 3 - Short Intro to Deep Learing

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1 like•292 views

This document discusses deep learning and its advantages over traditional machine learning approaches. It notes that deep learning methods can automatically discover representations from raw data through multiple levels of representation, whereas traditional approaches require manual feature engineering. The document uses the example of digit recognition to demonstrate how deep learning can achieve higher recognition rates than traditional approaches or simpler neural networks by learning representations from pixel data. Specifically, it suggests deep learning may be able to correctly recognize 10 out of 10 digits, compared to 8 or 9 out of 10 for other methods.

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•by DebasisMohanty37

13/08/15
5
Each image is composed of
28 x 28 = 784 pixels each
containing a grayscale
value between 0 and 255

13/08/15
6
This low-level representation uses a
vector of 784 features, each with values
between 0 and 255

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0
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A simple
representation learning
approach to the digit
recognition problem
could use a multi-
layer perceptron to
make predictions using
the low-level
representation
0
1
2
3
4
5
6
7
8
9

13/08/15
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0
1
2
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5
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8
9
This neural network
could manage to
correctly recognise
about 9 out of
every 10 digits
Deep-‐learning
methods
are
representa0on-‐
learning
methods
with
mul>ple
levels
of

representa>on,
obtained
by
composing
simple

but
non-‐linear
modules
that
each
transform
the

representa>on
at
one
level
(star>ng
with
the
raw

input)
into
a
representa>on
at
a
higher,
slightly

more
abstract
level.

[LeCun
etal,
2014]

Deep Learning
Yann LeCun, Yoshua Bengio & Geoffrey Hinton
http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html

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•by IIT Delhi

ansarinaorshamir

Handwritten Digit Recognition

The main objective of this paper is to recognize and predict handwritten digits from 0 to 9 where data set of 5000 examples of MNIST was given as input. As we know as every person has different style of writing digits humans can recognize easily but for computers it is comparatively a difficult task so here we have used neural network approach where in the machine will learn on itself by gaining experiences and the accuracy will increase based upon the experience it gains. The dataset was trained using feed forward neural network algorithm. The overall system accuracy obtained was 95.7% Jyoti Shinde | Chaitali Rajput | Prof. Mrunal Shidore | Prof. Milind Rane"Handwritten Digit Recognition" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd8384.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/8384/handwritten-digit-recognition/jyoti-shinde

•by ijtsrd

electronics & communication engineering

13/08/15
9
0
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3
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5
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8
9
0
1
2
3
4
5
6
7
8
9
A deep learning
appraoch could
manage to correctly
recognise about 10
out of every 10
digits

13/08/15
10
Deep
neural
networks
seem
to
brilliantly
address

the
selec0vity-‐invariance
dilemma
that
is

fundamental
to
all
eﬀorts
to
learn
to
classify

objects:
they
produce
representa>ons
that
are

selec>ve
to

the
aspects
of
the
image
that
are

important
for
discrimina>on,
but
that
are

invariant
to
irrelevant
aspects

Deep
networks
hold
records
for
problems
in

image
recogni0on,
speech
recogni0on,
and
text

classiﬁca0on
amongst
other
areas

Hardware

Data
Algorithms

Applica>ons

13/08/15
11
Thank You
Questions?
Fundamentals of Machine
Learning for Predictive Data
Analytics: Algorithms, Worked
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John D. Kelleher, Brian Mac Namee and
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www.machinelearningbook.com

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Artificial neural networks are models inspired by human nervous system that is capable of learning. One of the important applications of artificial neural network is character Recognition. Character Recognition finds its application in number of areas, such as banking, security products, hospitals, in robotics also. This paper is based on a system that recognizes a english numeral, given by the user, which is already trained on the features of the numbers to be recognized using NNT (Neural network toolbox) .The system has a neural network as its core, which is first trained on a database. The training of the neural network extracts the features of the English numbers and stores in the database. The next phase of the system is to recognize the number given by the user. The features of the number given by the user are extracted and compared with the feature database and the recognized number is displayed.

Deep learning for pose-invariant face detection in unconstrained environment

IJECEIAES

In the recent past, convolutional neural networks (CNNs) have seen resurgence and have performed extremely well on vision tasks. Visually the model resembles a series of layers each of which is processed by a function to form a next layer. It is argued that CNN first models the low level features such as edges and joints and then expresses higher level features as a composition of these low level features. The aim of this paper is to detect multi-view faces using deep convolutional neural network (DCNN). Implementation, detection and retrieval of faces will be obtained with the help of direct visual matching technology. Further, the probabilistic measure of the similarity of the face images will be done using Bayesian analysis. Experiment detects faces with ±90 degree out of plane rotations. Fine tuned AlexNet is used to detect pose invariant faces. For this work, we extracted examples of training from AFLW (Annotated Facial Landmarks in the Wild) dataset that involve 21K images with 24K annotations of the face.

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Report face recognition : ArganRecogn

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Deep learning applications, especially multilayer neural network models, result in network intrusion detection with high accuracy. This study proposes a model that combines a multilayer neural network with Dense Sparse Dense (DSD) multi-stage training to simultaneously improve the criteria related to the performance of intrusion detection systems on a comprehensive dataset UNSW-NB15. We conduct experiments on many neural network models such as Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), etc. to evaluate the combined efficiency with each model through many criteria such as accuracy, detection rate, false alarm rate, precision, and F1-Score.

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Neural network based numerical digits recognization using nnt in matlab

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Coordinate Systems in FME 101 - Webinar Slides

Safe Software

If you’ve ever had to analyze a map or GPS data, chances are you’ve encountered and even worked with coordinate systems. As historical data continually updates through GPS, understanding coordinate systems is increasingly crucial. However, not everyone knows why they exist or how to effectively use them for data-driven insights. During this webinar, you’ll learn exactly what coordinate systems are and how you can use FME to maintain and transform your data’s coordinate systems in an easy-to-digest way, accurately representing the geographical space that it exists within. During this webinar, you will have the chance to: - Enhance Your Understanding: Gain a clear overview of what coordinate systems are and their value - Learn Practical Applications: Why we need datams and projections, plus units between coordinate systems - Maximize with FME: Understand how FME handles coordinate systems, including a brief summary of the 3 main reprojectors - Custom Coordinate Systems: Learn how to work with FME and coordinate systems beyond what is natively supported - Look Ahead: Gain insights into where FME is headed with coordinate systems in the future Don’t miss the opportunity to improve the value you receive from your coordinate system data, ultimately allowing you to streamline your data analysis and maximize your time. See you there!

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Is your patent a vanity piece of paper for your office wall? Or is it a reliable, defendable, assertable, property right? The difference is often quality. Is your patent simply a transactional cost and a large pile of legal bills for your startup? Or is it a leverageable asset worthy of attracting precious investment dollars, worth its cost in multiples of valuation? The difference is often quality. Is your patent application only good enough to get through the examination process? Or has it been crafted to stand the tests of time and varied audiences if you later need to assert that document against an infringer, find yourself litigating with it in an Article 3 Court at the hands of a judge and jury, God forbid, end up having to defend its validity at the PTAB, or even needing to use it to block pirated imports at the International Trade Commission? The difference is often quality. Quality will be our focus for a good chunk of the remainder of this season. What goes into a quality patent, and where possible, how do you get it without breaking the bank? ** Episode Overview ** In this first episode of our quality series, Kristen Hansen and the panel discuss: ⦿ What do we mean when we say patent quality? ⦿ Why is patent quality important? ⦿ How to balance quality and budget ⦿ The importance of searching, continuations, and draftsperson domain expertise ⦿ Very practical tips, tricks, examples, and Kristen’s Musts for drafting quality applications https://www.aurorapatents.com/patently-strategic-podcast.html

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The presentation showcases the diverse real-world applications of Fused Deposition Modeling (FDM) across multiple industries: 1. **Manufacturing**: FDM is utilized in manufacturing for rapid prototyping, creating custom tools and fixtures, and producing functional end-use parts. Companies leverage its cost-effectiveness and flexibility to streamline production processes. 2. **Medical**: In the medical field, FDM is used to create patient-specific anatomical models, surgical guides, and prosthetics. Its ability to produce precise and biocompatible parts supports advancements in personalized healthcare solutions. 3. **Education**: FDM plays a crucial role in education by enabling students to learn about design and engineering through hands-on 3D printing projects. It promotes innovation and practical skill development in STEM disciplines. 4. **Science**: Researchers use FDM to prototype equipment for scientific experiments, build custom laboratory tools, and create models for visualization and testing purposes. It facilitates rapid iteration and customization in scientific endeavors. 5. **Automotive**: Automotive manufacturers employ FDM for prototyping vehicle components, tooling for assembly lines, and customized parts. It speeds up the design validation process and enhances efficiency in automotive engineering. 6. **Consumer Electronics**: FDM is utilized in consumer electronics for designing and prototyping product enclosures, casings, and internal components. It enables rapid iteration and customization to meet evolving consumer demands. 7. **Robotics**: Robotics engineers leverage FDM to prototype robot parts, create lightweight and durable components, and customize robot designs for specific applications. It supports innovation and optimization in robotic systems. 8. **Aerospace**: In aerospace, FDM is used to manufacture lightweight parts, complex geometries, and prototypes of aircraft components. It contributes to cost reduction, faster production cycles, and weight savings in aerospace engineering. 9. **Architecture**: Architects utilize FDM for creating detailed architectural models, prototypes of building components, and intricate designs. It aids in visualizing concepts, testing structural integrity, and communicating design ideas effectively. Each industry example demonstrates how FDM enhances innovation, accelerates product development, and addresses specific challenges through advanced manufacturing capabilities.

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The integration of programming into civil engineering is transforming the industry. We can design complex infrastructure projects and analyse large datasets. Imagine revolutionizing the way we build our cities and infrastructure, all by the power of coding. Programming skills are no longer just a bonus—they’re a game changer in this era. Technology is revolutionizing civil engineering by integrating advanced tools and techniques. Programming allows for the automation of repetitive tasks, enhancing the accuracy of designs, simulations, and analyses. With the advent of artificial intelligence and machine learning, engineers can now predict structural behaviors under various conditions, optimize material usage, and improve project planning.

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As a popular open-source library for analytics engineering, dbt is often used in combination with Airflow. Orchestrating and executing dbt models as DAGs ensures an additional layer of control over tasks, observability, and provides a reliable, scalable environment to run dbt models. This webinar will cover a step-by-step guide to Cosmos, an open source package from Astronomer that helps you easily run your dbt Core projects as Airflow DAGs and Task Groups, all with just a few lines of code. We’ll walk through: - Standard ways of running dbt (and when to utilize other methods) - How Cosmos can be used to run and visualize your dbt projects in Airflow - Common challenges and how to address them, including performance, dependency conflicts, and more - How running dbt projects in Airflow helps with cost optimization Webinar given on 9 July 2024

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Cybersecurity is a major concern in today's connected digital world. Threats to organizations are constantly evolving and have the potential to compromise sensitive information, disrupt operations, and lead to significant financial losses. Traditional cybersecurity techniques often fall short against modern attackers. Therefore, advanced techniques for cyber security analysis and anomaly detection are essential for protecting digital assets. This blog explores these cutting-edge methods, providing a comprehensive overview of their application and importance.

Transcript: Details of description part II: Describing images in practice - T...

BookNet Canada

This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator. Link to presentation recording and slides: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/ Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.

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Brian Mac Namee - Predict Webinar 3 - Short Intro to Deep Learing

1. 13/08/15 1 A Short(er) Introduc0on To Deep Learning Dr. Brian Mac Namee University College Dublin

2. 13/08/15 2 Deep Learning Google Trends: http://www.google.com/trends/ 2005 2007 2009 2011 2013 2015 Kaggle Digit Recogniser Contest https://www.kaggle.com/c/digit-recognizer MNIST Dataset from Yan LeCun http://yann.lecun.com/exdb/mnist/index.html

3. 13/08/15 3 The standard approach to using machine learning to build a system to recognise these different digits is to first engineer a high-level respresentation Percent filled: 0.37 Number of loops: 2 Direction 0: 0.2 Direction 1: 0.6 Direction 2: 0.1 Direction 3: 0.4 Percent filled: 0.11 Number of loops: 0 Direction 0: 0.1 Direction 1: 0.4 Direction 2: 0.0 Direction 3: 0.5 0 1 2 3 Percent filled: 0.29 Number of loops: 1 Direction 0: 0.2 Direction 1: 0.5 Direction 2: 0.4 Direction 3: 0.2 The standard approach to using machine learning to build a system to recognise these different digits is to first engineer a high-level respresentation Percent filled: 0.37 Number of loops: 2 Direction 0: 0.2 Direction 1: 0.6 Direction 2: 0.1 Direction 3: 0.4 Percent filled: 0.11 Number of loops: 0 Direction 0: 0.1 Direction 1: 0.4 Direction 2: 0.0 Direction 3: 0.5 0 1 2 3 Percent filled: 0.29 Number of loops: 1 Direction 0: 0.2 Direction 1: 0.5 Direction 2: 0.4 Direction 3: 0.2 Using this reperesentation (6 features) we could train a decision tree that would manage to correctly recognise about 8 out of every 10 digits

4. 13/08/15 4 Engineering representa0ons, is one of the most important and >me consuming jobs in most predic>ve analy>cs projects, and needs a blend of technical exper>se and domain exper>se Representa0on learning is a set of methods that allows a machine to be fed with raw data and to automa>cally discover the representa>ons needed for detec>on or classiﬁca>on [LeCun etal, 2014] Deep Learning Yann LeCun, Yoshua Bengio & Geoffrey Hinton http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html Rosenbla='s perceptron from 1957 was the earliest example of representa0on learning, and the ﬁrst neural network

5. 13/08/15 5 Each image is composed of 28 x 28 = 784 pixels each containing a grayscale value between 0 and 255

6. 13/08/15 6 This low-level representation uses a vector of 784 features, each with values between 0 and 255

7. 13/08/15 7 0 1 2 3 4 5 6 7 8 9 A simple representation learning approach to the digit recognition problem could use a multilayer perceptron to make predictions using the low-level representation 0 1 2 3 4 5 6 7 8 9

8. 13/08/15 8 0 1 2 3 4 5 6 7 8 9 This neural network could manage to correctly recognise about 9 out of every 10 digits Deep-‐learning methods are representa0on-‐ learning methods with mul>ple levels of representa>on, obtained by composing simple but non-‐linear modules that each transform the representa>on at one level (star>ng with the raw input) into a representa>on at a higher, slightly more abstract level. [LeCun etal, 2014] Deep Learning Yann LeCun, Yoshua Bengio & Geoffrey Hinton http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html

9. 13/08/15 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 A deep learning appraoch could manage to correctly recognise about 10 out of every 10 digits

10. 13/08/15 10 Deep neural networks seem to brilliantly address the selec0vity-‐invariance dilemma that is fundamental to all eﬀorts to learn to classify objects: they produce representa>ons that are selec>ve to the aspects of the image that are important for discrimina>on, but that are invariant to irrelevant aspects Deep networks hold records for problems in image recogni0on, speech recogni0on, and text classiﬁca0on amongst other areas Hardware Data Algorithms Applica>ons

11. 13/08/15 11 Thank You Questions? Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies John D. Kelleher, Brian Mac Namee and Aoife D'Arcy www.machinelearningbook.com

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