This document provides an overview of convolutional neural networks (CNNs). It explains that CNNs can learn spatial features from images using convolutional and pooling layers, whereas multilayer perceptrons struggle with images. CNNs use small filters that are convolved across the image to learn features, like edges or patterns. The filters are learned through backpropagation. Pooling layers then reduce spatial information to focus on prominent features. Together, convolutional and pooling layers allow CNNs to learn increasingly complex features from images hierarchically and overcome difficulties in training deep networks.
I developed a Convolutional Neural Network using Python. This particular CNN is able to identify the correct individual based solely off of a photo with the knowledge of facial recognition.
Build computer vision models to perform object detection and classification w...Bill Liu
event: https://learn.xnextcon.com/event/eventdetails/W20042918
video:
description: Computer Vision has received significant attention over the recent years, both within academia, and industry. As the state-of-the-art rapidly improves, the art-of-the-possible follows , offering innovative forms of computer vision applications for different scenarios.
In this talk, Ramine will cover the background and development of computer vision, and demonstrate how to use AWS to build robust, computer vision models to perform object detection and classification.
Key Takeaways:
Understand the history of Computer Vision
Learn how to use Amazon SageMaker to build and Deploy Computer Vision Models
How to orchestrate multiple models for implementing a real-world use case
I developed a Convolutional Neural Network using Python. This particular CNN is able to identify the correct individual based solely off of a photo with the knowledge of facial recognition.
Build computer vision models to perform object detection and classification w...Bill Liu
event: https://learn.xnextcon.com/event/eventdetails/W20042918
video:
description: Computer Vision has received significant attention over the recent years, both within academia, and industry. As the state-of-the-art rapidly improves, the art-of-the-possible follows , offering innovative forms of computer vision applications for different scenarios.
In this talk, Ramine will cover the background and development of computer vision, and demonstrate how to use AWS to build robust, computer vision models to perform object detection and classification.
Key Takeaways:
Understand the history of Computer Vision
Learn how to use Amazon SageMaker to build and Deploy Computer Vision Models
How to orchestrate multiple models for implementing a real-world use case
IMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHESVikash Kumar
IMAGE CLASSIFICATION USING KNN, RANDOM FOREST AND SVM ALGORITHM ON GLAUCOMA DATASETS AND EXPLAIN THE ACCURACY, SENSITIVITY, AND SPECIFICITY OF EACH AND EVERY ALGORITHMS
Movie Sentiment Analysis using Deep Learning RNNijtsrd
Sentimental analysis or opinion mining is the process of obtaining sentiments about a given textual data using various methods of deep learning algorithms. The analysis is used to determine the polarity of the data as either positive or negative. This classifications can help automate data representation in various sectors which has a public feedback structure. In this paper, we are going to perform sentiment analysis on the infamous IMDB database which consists of 50000 movie reviews, in which we perform training on 25000 instances and test it on 25000 to determine the performance of the model. The model uses a variant of RNN algorithm which is LSTM Long Short Term Memory which will help us a make a model which will decide the polarity between 0 and 1. This approach has an accuracy of 88.04 Nirsen Amal A | Vijayakumar A "Movie Sentiment Analysis using Deep Learning - RNN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42414.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42414/movie-sentiment-analysis-using-deep-learning--rnn/nirsen-amal-a
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
Machine learning in science and industry — day 4arogozhnikov
- tabular data approach to machine learning and when it didn't work
- convolutional neural networks and their application
- deep learning: history and today
- generative adversarial networks
- finding optimal hyperparameters
- joint embeddings
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/
Summary: Graphs are structures commonly used in computer science that model the interactions among entities. I will start from introducing the basic formulations of graph based machine learning, which has been a popular topic of research in the past decade and led to a powerful set of techniques. Particularly, I will show examples on how it acts as a generic data mining and predictive analytic tool. In the second part, I am going to discuss applications of such learning techniques in media analytics: (1) image analysis, where visually coherent objects are isolated from images; (2) social analysis of videos, where actors' social properties are predicted from videos. Materials in this part are based on our recent publications in highly selective venues (papers on https://sites.google.com/site/leiding2010/ ).
Bio: Lei Ding is a researcher making sense of large amounts of data in all media types. He currently works in Intent Media as a scientist, focusing on data analytics and applied machine learning in online advertising. Previously, he has worked in several research institutions including Columbia University, UIUC and IBM Research on digital / social media analysis and understanding. He received a Ph.D. degree in Computer Science and Engineering from The Ohio State University, where he was a Distinguished University Fellow.
A description of decision trees in machine learning. We explore ID3, C4.5 and CART algorithms, overfitting and how to fix it. We also discuss more scalable decision tree algorithms like SLIQ, CLOUDS and BOAT.
IMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHESVikash Kumar
IMAGE CLASSIFICATION USING KNN, RANDOM FOREST AND SVM ALGORITHM ON GLAUCOMA DATASETS AND EXPLAIN THE ACCURACY, SENSITIVITY, AND SPECIFICITY OF EACH AND EVERY ALGORITHMS
Movie Sentiment Analysis using Deep Learning RNNijtsrd
Sentimental analysis or opinion mining is the process of obtaining sentiments about a given textual data using various methods of deep learning algorithms. The analysis is used to determine the polarity of the data as either positive or negative. This classifications can help automate data representation in various sectors which has a public feedback structure. In this paper, we are going to perform sentiment analysis on the infamous IMDB database which consists of 50000 movie reviews, in which we perform training on 25000 instances and test it on 25000 to determine the performance of the model. The model uses a variant of RNN algorithm which is LSTM Long Short Term Memory which will help us a make a model which will decide the polarity between 0 and 1. This approach has an accuracy of 88.04 Nirsen Amal A | Vijayakumar A "Movie Sentiment Analysis using Deep Learning - RNN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42414.pdf Paper URL: https://www.ijtsrd.comcomputer-science/other/42414/movie-sentiment-analysis-using-deep-learning--rnn/nirsen-amal-a
In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.
Machine learning in science and industry — day 4arogozhnikov
- tabular data approach to machine learning and when it didn't work
- convolutional neural networks and their application
- deep learning: history and today
- generative adversarial networks
- finding optimal hyperparameters
- joint embeddings
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/
Summary: Graphs are structures commonly used in computer science that model the interactions among entities. I will start from introducing the basic formulations of graph based machine learning, which has been a popular topic of research in the past decade and led to a powerful set of techniques. Particularly, I will show examples on how it acts as a generic data mining and predictive analytic tool. In the second part, I am going to discuss applications of such learning techniques in media analytics: (1) image analysis, where visually coherent objects are isolated from images; (2) social analysis of videos, where actors' social properties are predicted from videos. Materials in this part are based on our recent publications in highly selective venues (papers on https://sites.google.com/site/leiding2010/ ).
Bio: Lei Ding is a researcher making sense of large amounts of data in all media types. He currently works in Intent Media as a scientist, focusing on data analytics and applied machine learning in online advertising. Previously, he has worked in several research institutions including Columbia University, UIUC and IBM Research on digital / social media analysis and understanding. He received a Ph.D. degree in Computer Science and Engineering from The Ohio State University, where he was a Distinguished University Fellow.
A description of decision trees in machine learning. We explore ID3, C4.5 and CART algorithms, overfitting and how to fix it. We also discuss more scalable decision tree algorithms like SLIQ, CLOUDS and BOAT.
A short presentation on Bayesian Classifiers. The PPT contains an example of Naive Bayes Classifier along with a short introduction to Bayesian Belief Networks
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
A Strategic Approach: GenAI in EducationPeter 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.
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.
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
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
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. What do Neural Networks Learn?
Every input to a neural network is a feature.
The neural network outputs whether the data point corresponding to
the input features belongs to class A or B.
In simple terms, a neural network builds a mapping from a non linear
combination of inputs (features) to the outputs (class labels).
In the case of image recognition, the input is an image itself. Usually
every pixel is a separate input to the neural network.
The MLP learns a non linear combination of pixel values to predict a
class label.
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3. Neural Networks and Image Recognition
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4. Neural Networks and Image Recognition
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5. Neural Networks and Image Recognition
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6. Neural Networks and Image Recognition
MLPs perform poorly in recognizing images.
MLPs cannot learn spatial correlations between images.
Prone to overfitting due to an abnormally large number of inputs and
weights.
There is no concept of ”features” because each feature is a pixel value.
How do we specify features in an image?
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7. Neural Networks and Image Recognition
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8. Neural Networks and Image Recognition
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9. Neural Networks and Image Recognition
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10. Neural Networks and Image Recognition
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11. Neural Networks and Image Recognition
It would be marvelous if the network could learn ”features” by itself...
which is what CNN does.
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12. Basic Idea behind CNN
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13. Filters and Convolutions
An image is basically a matrix of pixel values.
So it makes sense to define all operations on images in terms of oper-
ations of a matrix as well.
All operations such as smoothing, sharpening, blurring, edge detection
etc can be defined in terms of operations on a matrix.
For this, an operation is denoted by a smaller matrix called a filter.
The filter is moved over the image from left to right and top to bottom,
and the corresponding elements of the image and the filter are multi-
plied and added. The resultant value is the pixel value of the modified
image.
This operation is called as convolution.
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16. Edge Detection using Convolution
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17. Edge Detection using Convolution
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18. Edge Detection using Convolution
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19. Learning Low Level Features in CNN
The first step in a CNN is to learn the low level features such as edges
from the input image.
This is done using a Convolutional Layer.
For this, a filter is moved over the entire image as in the case of
convolution.
Are the filter weights static?
NO.
The filter weights are randomly initialized and the weights are updated
using backpropagation till the weights stabilize.
This means we have no idea which feature (horizontal edge, vertical
edge, slanting lines...) a particular filter learns to recognize.
A number of filters are used in a CNN, and each filter learns to recognize
a particular feature.
Each filter is said to output a Feature Map.
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20. Peculiarities of the Convolutional Layer
Local Receptive Fields
Not fully connected as an MLP
Shared Weights
Learn to recognize a feature irrespective of its absolute location
Fewer parameters, hence less prone to overfitting
ReLu activation function
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24. Pooling
The convolutional layers recognize the presence of features in the im-
age.
However, the output of these layers also contain positional information
i.e. where these features were found.
Usually positional information acts as a burden in classification. We
want relative positional information of features, not where the absolute
position of a feature is.
The Pooling Layer removes positional information from the output of
the Convolutional Layers.
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26. That’s It!!!
A CNN is essentially comprised of multiple convolutional and pooling
layers one after the other.
Each successive layer recognizes more sophisticated features using low
level features detected by the previous layers.
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28. A Note on the Output Layer
While all the other layers are only partially connected, the output layer
is fully connected.
The number of nodes in the output layer is usually equally to the
number of classes in the classification problem. For example, if you
want to classify cats, dogs, wolves and foxes, the output layer will have
four nodes.
The nodes in the output layer have a special activation function, called
Softmax Activation Function.
aL
j =
exL
j
k exL
k
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29. Softmax Activation
Softmax Activation forms a probability distribution, and gives the prob-
ability that the given input belongs to class j.
Along with a new log likelihood cost function given by
C = −ln aL
j
the network can counter learning slowdown as well.
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30. A Note on Overfitting
Even though CNN uses much fewer weights than MLP, it can still suffer
from overfitting.
Techniques to counter overfitting, such as regularization, validation,
acquiring new data etc. can still be used here.
Another technique usually used to reduce the effects of overfitting is
the use of Ensemble Classifiers.
Similar to Random Forests, we can use a number of neural networks
(CNN or MLP), train them separately and employ a majority voting to
decide the class during testing.
However, MLP or CNN need a lot more time to train and hence main-
taining multiple models is infeasible.
Rather, there is a technique that tries to use only one physical model,
but train multiple virtual models in it.
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31. Dropout
The idea behind dropout is to
randomly disable or drop 50%
of the neurons during different
stages of training.
This is done so that the neu-
ral network as a whole becomes
more robust.
Virtually, we are training mul-
tiple neural networks for the
same input, which can help in
reducing overfitting.
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32. How does CNN overcome the difficulties in training Deep
Networks?
Learning Slowdown → Softmax Activation function in the output layer
+ Log Likelihood Cost
Vanishing Gradient → ReLu Activation Function in convolutional layers
Overfitting → Shared Weights and Biases, Regularization, Dropout
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