The document provides an introduction to machine learning and deep learning. It defines machine learning as a field of artificial intelligence that uses statistical techniques to give computer systems the ability to "learn" with data, without being explicitly programmed. The document discusses different types of machine learning including supervised learning, unsupervised learning, and reinforcement learning. It also introduces deep learning and convolutional neural networks, describing how deep learning can be used to perform complex tasks like image recognition.
This is the slide that Terry. T. Um gave a presentation at Kookmin University in 22 June, 2014. Feel free to share it and please let me know if there is some misconception or something.
(http://t-robotics.blogspot.com)
(http://terryum.io)
Machine Learning and Deep Contemplation of DataJoel Saltz
Spatio temporal data analytics - Generation of Features
1) Sanity Checking and Data Cleaning, 2) Qualitative Exploration, 3) Descriptive Statistics, 4) Classification, 5) Identification of Interesting Phenomena, 6) Prediction, 7) Control and 8)
Save Data for Later (Compression).
Detailed example from Precision Medicine; Pathomics, Radiomics.
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
This is the slide that Terry. T. Um gave a presentation at Kookmin University in 22 June, 2014. Feel free to share it and please let me know if there is some misconception or something.
(http://t-robotics.blogspot.com)
(http://terryum.io)
Machine Learning and Deep Contemplation of DataJoel Saltz
Spatio temporal data analytics - Generation of Features
1) Sanity Checking and Data Cleaning, 2) Qualitative Exploration, 3) Descriptive Statistics, 4) Classification, 5) Identification of Interesting Phenomena, 6) Prediction, 7) Control and 8)
Save Data for Later (Compression).
Detailed example from Precision Medicine; Pathomics, Radiomics.
In this presentation we described important things about Image processing and computer vision. If you have any query about this presentation then feels free to visit us at:
http://www.siliconmentor.com/
#6 PyData Warsaw: Deep learning for image segmentationMatthew Opala
Deep learning techniques ignited a great progress in many computer vision tasks like image classification, object detection, and segmentation. Almost every month a new method is published that achieves state-of-the-art result on some common benchmark dataset. In addition to that, DL is being applied to new problems in CV.
In the talk we’re going to focus on DL application to image segmentation task. We want to show the practical importance of this task for the fashion industry by presenting our case study with results achieved with various attempts and methods.
These slides accompanied a demo of Deeplearning4j at the SF Data Mining Meetup hosted by Trulia.
http://www.meetup.com/Data-Mining/events/212445872/
Deep-learning is useful in detecting identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; and recognizing faces and voices.
Deeplearning4j is an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. It includes a distributed deep-learning framework and a normal deep-learning framework; i.e. it runs on a single thread as well. Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure. The distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models.
The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.
Finally, Deeplearning4j integrates with GPUs. A stable version was released in October.
Computer vision has started to achieve some very impressive results over the last 5-10 years. It is now possible to quickly and reliably detect faces, recognize and localize target images, and even classify pictures of objects into generic categories. Unfortunately, knowledge of these techniques remains largely confined to academia. In this session we’ll go over some of the tools available, placing an emphasis on exploring the ideas and algorithms behind their design.
To show how these components can be put together, a sample system will be developed over the course of the presentation. Starting with standard image descriptors, we’ll first see how to do direct image recognition. We’ll then extend that into a simple object classifier, which will be able to distinguish (for example) between images which contain a bicycle and those that don’t.
COM2304: Introduction to Computer Vision & Image Processing Hemantha Kulathilake
At the end of this lesson, you should be able to;
Describe image.
Describe digital image processing and computer vision.
Compare and Contrast image processing and computer vision.
Describe image sources.
Identify the array of imaging application under the EM Image source.
Describe the components of Image processing system and computer vision system.
Machine Learning is all the rage today with many different options and paradigms. This session will walk through the basics of Machine Learning and show how to get started with the open source Spark ML framework. Through Scala code examples you will learn how to build and deploy learning systems like recommendation engines.
What is "deep learning" and why is it suddenly so popular? In this talk I explore how Deep Learning provides a convenient framework for expressing learning problems and using GPUs to solve them efficiently.
An introduction to Machine Learning (and a little bit of Deep Learning)Thomas da Silva Paula
25-min talk about Machine Learning and a little bit of Deep Learning. Starts with some basic definitions (Supervised and Unsupervised Learning). Then, neural networks basic functionality is explained, ending up in Deep Learning and Convolutional Neural Networks.
Machine Learning Meetup that happened in Porto Alegre, Brazil.
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.
Deep learning goes beyond the traditional machine learning of big data and analytics. In this session, we will review the AWS offering, Amazon Machine Learning, and the AWS GPU-intensive family of servers that run native machine learning and deep-learning algorithms. We will also cover some basic deep-learning algorithms using open source software. Session sponsored by Day1 Solutions.
Transform your Business with AI, Deep Learning and Machine LearningSri Ambati
Video: https://www.youtube.com/watch?v=R3IXd1iwqjc
Meetup: http://www.meetup.com/SF-Bay-ACM/events/231709894/
In this talk, Arno Candel presents a brief history of AI and how Deep Learning and Machine Learning techniques are transforming our everyday lives. Arno will introduce H2O, a scalable open-source machine learning platform, and show live demos on how to train sophisticated machine learning models on large distributed datasets. He will show how data scientists and application developers can use the Flow GUI, R, Python, Java, Scala, JavaScript and JSON to build smarter applications, and how to take them to production. He will present customer use cases from verticals including insurance, fraud, churn, fintech, and marketing.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Data Science - Part XVII - Deep Learning & Image ProcessingDerek Kane
This lecture provides an overview of Image Processing and Deep Learning for the applications of data science and machine learning. We will go through examples of image processing techniques using a couple of different R packages. Afterwards, we will shift our focus and dive into the topics of Deep Neural Networks and Deep Learning. We will discuss topics including Deep Boltzmann Machines, Deep Belief Networks, & Convolutional Neural Networks and finish the presentation with a practical exercise in hand writing recognition technique.
Optimization techniques in formulation Development- Plackett Burmann Design a...D.R. Chandravanshi
It is the process of finding the best way of using the existing resources while taking in to the account of all the factors that influences decisions in any experiment.
The objective of designing quality formulation is achieved by various optimization techniques.
In Pharmacy word “optimization” is found in the literature referring to study of the formula. In formulation development process generally experiments by a series of logical steps, carefully controlling the variables and changing one at a time until satisfactory results are obtained.
#6 PyData Warsaw: Deep learning for image segmentationMatthew Opala
Deep learning techniques ignited a great progress in many computer vision tasks like image classification, object detection, and segmentation. Almost every month a new method is published that achieves state-of-the-art result on some common benchmark dataset. In addition to that, DL is being applied to new problems in CV.
In the talk we’re going to focus on DL application to image segmentation task. We want to show the practical importance of this task for the fashion industry by presenting our case study with results achieved with various attempts and methods.
These slides accompanied a demo of Deeplearning4j at the SF Data Mining Meetup hosted by Trulia.
http://www.meetup.com/Data-Mining/events/212445872/
Deep-learning is useful in detecting identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; and recognizing faces and voices.
Deeplearning4j is an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. It includes a distributed deep-learning framework and a normal deep-learning framework; i.e. it runs on a single thread as well. Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure. The distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models.
The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.
Finally, Deeplearning4j integrates with GPUs. A stable version was released in October.
Computer vision has started to achieve some very impressive results over the last 5-10 years. It is now possible to quickly and reliably detect faces, recognize and localize target images, and even classify pictures of objects into generic categories. Unfortunately, knowledge of these techniques remains largely confined to academia. In this session we’ll go over some of the tools available, placing an emphasis on exploring the ideas and algorithms behind their design.
To show how these components can be put together, a sample system will be developed over the course of the presentation. Starting with standard image descriptors, we’ll first see how to do direct image recognition. We’ll then extend that into a simple object classifier, which will be able to distinguish (for example) between images which contain a bicycle and those that don’t.
COM2304: Introduction to Computer Vision & Image Processing Hemantha Kulathilake
At the end of this lesson, you should be able to;
Describe image.
Describe digital image processing and computer vision.
Compare and Contrast image processing and computer vision.
Describe image sources.
Identify the array of imaging application under the EM Image source.
Describe the components of Image processing system and computer vision system.
Machine Learning is all the rage today with many different options and paradigms. This session will walk through the basics of Machine Learning and show how to get started with the open source Spark ML framework. Through Scala code examples you will learn how to build and deploy learning systems like recommendation engines.
What is "deep learning" and why is it suddenly so popular? In this talk I explore how Deep Learning provides a convenient framework for expressing learning problems and using GPUs to solve them efficiently.
An introduction to Machine Learning (and a little bit of Deep Learning)Thomas da Silva Paula
25-min talk about Machine Learning and a little bit of Deep Learning. Starts with some basic definitions (Supervised and Unsupervised Learning). Then, neural networks basic functionality is explained, ending up in Deep Learning and Convolutional Neural Networks.
Machine Learning Meetup that happened in Porto Alegre, Brazil.
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.
Deep learning goes beyond the traditional machine learning of big data and analytics. In this session, we will review the AWS offering, Amazon Machine Learning, and the AWS GPU-intensive family of servers that run native machine learning and deep-learning algorithms. We will also cover some basic deep-learning algorithms using open source software. Session sponsored by Day1 Solutions.
Transform your Business with AI, Deep Learning and Machine LearningSri Ambati
Video: https://www.youtube.com/watch?v=R3IXd1iwqjc
Meetup: http://www.meetup.com/SF-Bay-ACM/events/231709894/
In this talk, Arno Candel presents a brief history of AI and how Deep Learning and Machine Learning techniques are transforming our everyday lives. Arno will introduce H2O, a scalable open-source machine learning platform, and show live demos on how to train sophisticated machine learning models on large distributed datasets. He will show how data scientists and application developers can use the Flow GUI, R, Python, Java, Scala, JavaScript and JSON to build smarter applications, and how to take them to production. He will present customer use cases from verticals including insurance, fraud, churn, fintech, and marketing.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Data Science - Part XVII - Deep Learning & Image ProcessingDerek Kane
This lecture provides an overview of Image Processing and Deep Learning for the applications of data science and machine learning. We will go through examples of image processing techniques using a couple of different R packages. Afterwards, we will shift our focus and dive into the topics of Deep Neural Networks and Deep Learning. We will discuss topics including Deep Boltzmann Machines, Deep Belief Networks, & Convolutional Neural Networks and finish the presentation with a practical exercise in hand writing recognition technique.
Optimization techniques in formulation Development- Plackett Burmann Design a...D.R. Chandravanshi
It is the process of finding the best way of using the existing resources while taking in to the account of all the factors that influences decisions in any experiment.
The objective of designing quality formulation is achieved by various optimization techniques.
In Pharmacy word “optimization” is found in the literature referring to study of the formula. In formulation development process generally experiments by a series of logical steps, carefully controlling the variables and changing one at a time until satisfactory results are obtained.
To buy MBA assignments please use below link
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Bio-inspired Algorithms for Evolving the Architecture of Convolutional Neural...Ashray Bhandare
In this thesis, three bio-inspired algorithms viz. genetic algorithm, particle swarm optimizer (PSO) and grey wolf optimizer (GWO) are used to optimally determine the architecture of a convolutional neural network (CNN) that is used to classify handwritten numbers. The CNN is a class of deep feed-forward network, which have seen major success in the field of visual image analysis. During training, a good CNN architecture is capable of extracting complex features from the given training data; however, at present, there is no standard way to determine the architecture of a CNN. Domain knowledge and human expertise are required in order to design a CNN architecture. Typically architectures are created by experimenting and modifying a few existing networks.
The bio-inspired algorithms determine the exact architecture of a CNN by evolving the various hyperparameters of the architecture for a given application. The proposed method was tested on the MNIST dataset, which is a large database of handwritten digits that is commonly used in many machine-learning models. The experiment was carried out on an Amazon Web Services (AWS) GPU instance, which helped to speed up the experiment time. The performance of all three algorithms was comparatively studied. The results show that the bio-inspired algorithms are capable of generating successful CNN architectures. The proposed method performs the entire process of architecture generation without any human intervention.
A tutorial on applying artificial neural networks and geometric brownian moti...eSAT Journals
Abstract Several challenges in the engineering or financial world can be resolved with a proper handle on data. Amongst other applications in engineering, system identification and parameter estimation are widely used in developing control strategies for automation. In this domain, there would be requirements to design an adaptive control system. In order to design an adaptive control system, an adaptive model needs to be estimated or identified. This is generally done by studying the data and creating a transfer function. In the process, regression, artificial neural networks (ANN), random walk theory and Markov chain estimates are used to understand a time series and create a model. While some of these processes are stationary, some are non-stationary. These methods are chosen based on the nature and availability of historical data. One of the issues that always remain is which method is appropriate for a certain application. The objective of this tutorial is to illustrate how artificial neural network and Geometric Brownian motion can be used in this regard. An attempt is made to predict the future price of a stock of a corporation. Stock prices are an example for a stochastic time series. Initially, an artificial neural network is used to predict the stock price. The network is designed as a Multi layer Back propagation type network. Profit over earnings and S&P are used as inputs. Thereafter, Geometric Brownian motion is explained and used on the same dataset to come up with its predictions. The results from both neural network and geometric Brownian motion are compared. Key Words: Artificial Neural Network, Geometric Brownian Motion, Stochastic time series, and stock price prediction
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
2. Agenda
What is the computer?
Introduction to machine learning
Applications on machine learning
Introduction to deep learning
Convolutional Neural Networks
How to build a computer to start deep learning
Deep learning Tools
Companies and deep learning
2
3. What is computer?
A computer is a machine that understands zeros and ones only.
The computer can perform complex mathematical and arithmetic operations
very fast.
It performs this using a combination of circuits and logic gates.
3
4. Comment on the following
What is the result?
50 + 30 ?
4
5. Comment on the following
What is the result?
58945 + 78954?
5
9. Comment on the following
Can you write a program to compute the
power ?
9
10. Comment on the following
Can you write a program to recognize
faces or recognize brands of cars?
10
11. Conclusion
Computers are very powerful and accurate in calculations, they can perform
any arithmetic operation faster than any human being.
Humans are faster and more accurate than computers in recognition tasks.
Do you know any one who could compute 787452665 * 75767487 in just
seconds?
Are the machines capable recognize and to reach human level accuracy in
recognition ?
11
12. What is Machine Learning?
“A computer program is said to learn from experience E with respect to some
task T and some performance measure P, if its performance on T, as
measured by P, improves with experience E.” -- Tom Mitchell, Carnegie
Mellon University
12
13. What is Machine Learning?
Machine learning is a discipline of AI
It is a series of techniques that are used to make the computer smarter .
ML solves problems that cannot be solved by numerical means alone.
Machine learning is empowering a lot of technologies used today.
Among the different types of ML tasks, a crucial distinction is drawn between
supervised and unsupervised learning:
Supervised machine learning: The program is “trained” on a pre-defined set of
“training examples”, which then facilitate its ability to reach an accurate
conclusion when given new data.
Unsupervised machine learning: The program is given a bunch of data and must
find patterns and relationships therein.
13
14. Supervised Machine Learning
In the majority of supervised learning applications, the ultimate goal is to
develop a finely tuned predictor function ℎ(𝑥) (sometimes called the
“hypothesis”)
Given input data 𝑥 about a certain domain (say, square footage of a house), it
will accurately predict some interesting value h 𝑥
In practice, x almost always represents multiple data points. So, for example,
a housing price predictor might take not only square-footage (x1) but also
number of bedrooms (x2), number of bathrooms (x3), number of floors (x4),
year built (x5), zip code (x6), and so forth. Determining which inputs to use is
an important part of ML design. However, for the sake of explanation, it is
easiest to assume a single input value is used.
14
15. So let’s say our simple predictor has this form:
Where 𝜃0 and 𝜃1 are constants. Our goal is to find the perfect values 𝜃0 and
𝜃1 to make our predictor work as well as possible.
To make our program learn 𝜃0 and 𝜃1 we should update them according to
error analysis which is called mean square error.
The weights are updated using gradient decent algorithm 15
18. Unsupervised Learning
Unsupervised learning typically is tasked with finding relationships within
data.
There are no training examples used in this process.
Instead, the system is given a set data and tasked with finding patterns and
correlations therein.
Examples: astronomical data analysis, social network analysis and cocktail
party problem and news grouping
18
19. Applications on machine learning
Spam filtering: Classifying emails as spam or non-spam
Weather forecast: Machine learning is applied in weather forecasting
software to improve the quality of the forecast.
Anti-virus: Machine learning is used in Anti-virus software's to improve
detection of malicious software on computer devices.
Personal Assistants: Siri & Cortana
Classifying a tumor as benign tumor or malignant tumor.
19
20. Deep Learning
Deep Learning is a subfield of machine learning concerned with algorithms
inspired by the structure and function of the brain called artificial neural
networks.
Andrew Ng: “very large neural networks we can now have and … huge
amounts of data that we have access to”
Andrew Ng: “for most flavors of the old generations of learning algorithms …
performance will plateau. … deep learning … is the first class of algorithms …
that is scalable. … performance just keeps getting better as you feed them
more data”
20
22. Deep Learning
Deep learning is set of techniques that in some cases could reach human
accuracy in recognition !
The most common used and the most famous architecture is convolutional
neural network.
There are some other techniques like:
LSTM (Long Short Term Memory)
Residual Neural Networks
Autoencoders
Generative Adversarial Networks.
We will focus on convolution neural networks.
22
23. A toy ConvNet: X’s and O’s
X or OCNN
A two-dimensional
array of pixels
28. -1 -1 -1 -1 -1 -1 -1 -1 -1
-1 X -1 -1 -1 -1 X X -1
-1 X X -1 -1 X X -1 -1
-1 -1 X 1 -1 1 -1 -1 -1
-1 -1 -1 -1 1 -1 -1 -1 -1
-1 -1 -1 1 -1 1 X -1 -1
-1 -1 X X -1 -1 X X -1
-1 X X -1 -1 -1 -1 X -1
-1 -1 -1 -1 -1 -1 -1 -1 -1
What computers see
88. How to build a computer to start deep
learning ?
Deep learning techniques’ are resources hungry.
They require huge computing power to give you good performance.
Imagine we have 10 convolution layers of:
256 Conv Filters * Image(1000, 1000)
88
89. How to build a computer to start deep
learning ?
This is pretty huge !.
If you ran this on a CPU it would take 1 day to process 60 000 training images
on I7 processor!
What if the network is deeper than 20 layers and more complex than 256 conv
filters / layer?
The network in this case may take days or even weeks to finish!!
89
90. So, what is the solution ?
In each layer in a CNN we are convolving the image with different filters.
What if we could convolve the image with different filters parallel in the
same time?
The solution is to use GPU!
90
91. Comment on the following
91
How many cores inside a CPU ?
How many cores inside a GPU?
92. What are GPUs brands available out
there?
There are two famous companies:
Unfortunately, AMD GPUs can’t be used for deep learning.
Nvidia GPUs only can be used
That’s because Nvidia provides tools and support for deep learning geeks.
There is a library called NVIDIA Cuda Toolkit
92
94. Nvidia GPUs
Almost all Nvidia’s GPUs will do the job.
You must check if it is CUDA enabled or not.
You can check online. Just search for CUDA supported GPUs.
Some of CUDA supported GPUs: TITAN X, Geforce GTX 1080, Geforce GTX 1070,
Geforce GTX 1060, Geforce GTX 1050, Geforce GTX 980, Geforce GTX 970,
Geforce GTX 960, Geforce GTX 9xx M Series, Geforce GTX 6xx Series, Geforce GTX
6xx M Series.
You can always use your CPU if you don’t have Nvidia GPU or if you don’t have
GPU at all!.
94
95. Deep Learning Tools
After building your computer and configuring it for deep learning, you need
the right tools and APIs to start coding
There are plenty of languages that can be used:
Matlab
Python
C++
C#
Java
And so many…
95
96. Deep Learning Tools
Deep learning frameworks:
NVIDIA CUDA programming APIs.
Tensorflow (Google’s library) for python.
Theano for python.
Torch (Facebook’s library) for Lua.
Caffe for python.
CNTK (Microsoft’s library) for python.
Keras to simplify coding for tensorflow and theano.
96
97. Deep Learning and Companies
Many Companies use deep learning on a daily basis.
Facebook: facebook auto tagger, videos and photos auto caption, post
analyzer,
DeepMind (acquired by google): AlphaGo and many
Amazon
97
99. References
Coursera’s Machine Learning course by Andrew Ng
CS231n: Convolutional Neural Networks for Visual Recognition
Setup a Deep Learning Environment on Windows (Theano & Keras with
GPU Enabled)
ConvNets Visualization
How do Convolutional Neural Networks work?
Rana el Kaliouby, Co-founder, CEO at Affectiva
Hussein Mehanna
99