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The document discusses the Minkowski sum, which is an operation that combines two sets in 2D geometry by translating one set along the border of the other. It provides examples of applying the Minkowski sum to polygons and discs. The Minkowski sum has applications in motion planning to determine if a moving object will collide with obstacles. It can be computed for convex polygons by taking every vertex combination, and for general polygons by decomposition or convolution methods.

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Computer Vision – From traditional approaches to deep neural networks

Event: GDG Munich February Meetup: Machine Learning, 27.02.2018
Speaker: Stanislav Frolov, inovex
Mehr Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/
Mehr Tech-Artikel im inovex Blog: https://www.inovex.de/blog

computer graphics

This document is a lecture outline for an introduction to computer graphics course. It outlines the course information and administrative details, provides an overview of topics to be covered including graphics systems, techniques, operations and a mathematical review. It also defines computer graphics, discusses image processing and analysis, and explains why computer graphics is an important field due to advances in computing power, visualization, and interaction capabilities.

Matrix decomposition and_applications_to_nlp

This document provides an overview of matrix decomposition techniques for dimensionality reduction and topic modeling, specifically principal component analysis (PCA), singular value decomposition (SVD), latent semantic analysis (LSA), and non-negative matrix factorization (NMF). PCA and SVD are introduced as mathematical techniques to reduce dimensions while preserving variance/information. LSA and NMF are described as applying SVD and NMF respectively to text data to derive topic models from the latent semantic space. Examples of implementing these techniques in Python are also provided.

Mandelbrot

The document discusses fractals and the Mandelbrot set. It defines a fractal as an iterated pattern that displays self-similarity, like the Koch curve or Sierpinski triangle. The Mandelbrot set is the set of complex numbers where the sequence zn+1 = zn^2 + c remains bounded. While the Mandelbrot set itself is not a fractal, the border of the set has a Hausdorff dimension exceeding its topological dimension, making it a fractal.

Singular Value Decomposition Image Compression

- SVD image compression works by decomposing an image matrix into three matrices - two orthogonal matrices containing singular vectors and a diagonal matrix containing singular values. The singular values are ordered from highest to lowest.
- Compression is achieved by only keeping the first few singular values, which contain most of the image information, and discarding the smaller values. This approximates the original matrix while reducing storage needs.
- The tradeoff is between compression ratio/file size and image quality - smaller values of k retain fewer singular values and compress more but quality deteriorates, while larger k improves quality at the cost of file size. An optimal k value balances these factors.

Linear models for classification

This document provides an overview of linear models for classification. It discusses discriminant functions including linear discriminant analysis and the perceptron algorithm. It also covers probabilistic generative models that model class-conditional densities and priors to estimate posterior probabilities. Probabilistic discriminative models like logistic regression directly model posterior probabilities using maximum likelihood. Iterative reweighted least squares is used to optimize logistic regression since there is no closed-form solution.

Normal Distribution.pdf

This document summarizes key concepts from Chapter 6 of a statistics textbook on the normal distribution. It introduces the normal distribution and its properties, including that a normal density curve has an area of 1 and the percentage of observations within a range equals the area under the curve. It defines a normally distributed variable as having a normal distribution shape and discusses standardizing normal distributions. Various procedures and facts are presented for working with normal distributions, including finding percentages of observations using the standard normal curve and assessing normality with normal probability plots.

Lasso and ridge regression

The document discusses different types of linear regression models including simple linear regression, multiple linear regression, ridge regression, lasso regression, and elastic net regression. It explains the concepts of slope, intercept, underfitting, overfitting, and regularization techniques used to constrain model weights. Specifically, it describes how ridge regression uses an L2 penalty, lasso regression uses an L1 penalty, and elastic net uses a combination of L1 and L2 penalties to regularize linear regression models and reduce overfitting.

Computer Vision – From traditional approaches to deep neural networks

Event: GDG Munich February Meetup: Machine Learning, 27.02.2018
Speaker: Stanislav Frolov, inovex
Mehr Tech-Vorträge: https://www.inovex.de/de/content-pool/vortraege/
Mehr Tech-Artikel im inovex Blog: https://www.inovex.de/blog

computer graphics

This document is a lecture outline for an introduction to computer graphics course. It outlines the course information and administrative details, provides an overview of topics to be covered including graphics systems, techniques, operations and a mathematical review. It also defines computer graphics, discusses image processing and analysis, and explains why computer graphics is an important field due to advances in computing power, visualization, and interaction capabilities.

Matrix decomposition and_applications_to_nlp

This document provides an overview of matrix decomposition techniques for dimensionality reduction and topic modeling, specifically principal component analysis (PCA), singular value decomposition (SVD), latent semantic analysis (LSA), and non-negative matrix factorization (NMF). PCA and SVD are introduced as mathematical techniques to reduce dimensions while preserving variance/information. LSA and NMF are described as applying SVD and NMF respectively to text data to derive topic models from the latent semantic space. Examples of implementing these techniques in Python are also provided.

Mandelbrot

The document discusses fractals and the Mandelbrot set. It defines a fractal as an iterated pattern that displays self-similarity, like the Koch curve or Sierpinski triangle. The Mandelbrot set is the set of complex numbers where the sequence zn+1 = zn^2 + c remains bounded. While the Mandelbrot set itself is not a fractal, the border of the set has a Hausdorff dimension exceeding its topological dimension, making it a fractal.

Singular Value Decomposition Image Compression

- SVD image compression works by decomposing an image matrix into three matrices - two orthogonal matrices containing singular vectors and a diagonal matrix containing singular values. The singular values are ordered from highest to lowest.
- Compression is achieved by only keeping the first few singular values, which contain most of the image information, and discarding the smaller values. This approximates the original matrix while reducing storage needs.
- The tradeoff is between compression ratio/file size and image quality - smaller values of k retain fewer singular values and compress more but quality deteriorates, while larger k improves quality at the cost of file size. An optimal k value balances these factors.

Linear models for classification

This document provides an overview of linear models for classification. It discusses discriminant functions including linear discriminant analysis and the perceptron algorithm. It also covers probabilistic generative models that model class-conditional densities and priors to estimate posterior probabilities. Probabilistic discriminative models like logistic regression directly model posterior probabilities using maximum likelihood. Iterative reweighted least squares is used to optimize logistic regression since there is no closed-form solution.

Normal Distribution.pdf

This document summarizes key concepts from Chapter 6 of a statistics textbook on the normal distribution. It introduces the normal distribution and its properties, including that a normal density curve has an area of 1 and the percentage of observations within a range equals the area under the curve. It defines a normally distributed variable as having a normal distribution shape and discusses standardizing normal distributions. Various procedures and facts are presented for working with normal distributions, including finding percentages of observations using the standard normal curve and assessing normality with normal probability plots.

Lasso and ridge regression

The document discusses different types of linear regression models including simple linear regression, multiple linear regression, ridge regression, lasso regression, and elastic net regression. It explains the concepts of slope, intercept, underfitting, overfitting, and regularization techniques used to constrain model weights. Specifically, it describes how ridge regression uses an L2 penalty, lasso regression uses an L1 penalty, and elastic net uses a combination of L1 and L2 penalties to regularize linear regression models and reduce overfitting.

K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...

This K-Means clustering algorithm presentation will take you through the machine learning introduction, types of clustering algorithms, k-means clustering, how does K-Means clustering work and at least explains K-Means clustering by taking a real life use case. This Machine Learning algorithm tutorial video is ideal for beginners to learn how K-Means clustering work.
Below topics are covered in this K-Means Clustering Algorithm presentation:
1. Types of Machine Learning?
2. What is K-Means Clustering?
3. Applications of K-Means Clustering
4. Common distance measure
5. How does K-Means Clustering work?
6. K-Means Clustering Algorithm
7. Demo: k-Means Clustering
8. Use case: Color compression
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -

Computer vision - edge detection

This document summarizes a lecture on edge detection in computer vision. It discusses how edges can be detected by finding places of rapid change in image intensity using derivatives and gradient filters like the Sobel operator. It also covers non-maximum suppression to thin edges and Canny edge detection, which uses two thresholds to link edges after filtering the image with derivatives of Gaussians. The Canny edge detector is presented as the standard computer vision pipeline for edge detection.

5 geometric-modeling-ppt-university-of-victoria

1) Geometric modeling is a fundamental CAD technique that represents objects using points, lines, curves, surfaces or solids.
2) Early techniques included wireframe and surface modeling but they were ambiguous and lacked topological data.
3) Solid modeling techniques like CSG and B-Rep overcome these issues by representing objects unambiguously using their volume and topology.
4) Feature-based modeling further advanced CAD by modeling objects parametrically using high-level features like holes and rounds.

Graph Representation Learning

Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. However, traditionally machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph. In this talk I will discuss methods that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. I will provide a conceptual review of key advancements in this area of representation learning on graphs, including random-walk based algorithms, and graph convolutional networks.

Linear Regression Analysis | Linear Regression in Python | Machine Learning A...

This Linear Regression in Machine Learning Presentation will help you understand the basics of Linear Regression algorithm - what is Linear Regression, why is it needed and how Simple Linear Regression works with solved examples, Linear regression analysis, applications of Linear Regression and Multiple Linear Regression model. At the end, we will implement a use case on profit estimation of companies using Linear Regression in Python. This Machine Learning presentation is ideal for beginners who want to understand Data Science algorithms as well as Machine Learning algorithms.
Below topics are covered in this Linear Regression Machine Learning Tutorial:
1. Introduction to Machine Learning
2. Machine Learning Algorithms
3. Applications of Linear Regression
4. Understanding Linear Regression
5. Multiple Linear Regression
6. Use case - Profit estimation of companies
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - - -
Who should take this Machine Learning Training Course?
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
- - - - - -

Matrix and its operations

Matrices can be added, subtracted, and multiplied according to certain rules.
- Matrices can only be added or subtracted if they are the same size. The sum or difference of matrices A and B yields a matrix C of the same size.
- Matrices can be multiplied by a scalar. Multiplying a matrix A by a scalar k results in a new matrix kA where each element is multiplied by k.
- Matrix multiplication allows combining information from two matrices but has specific rules regarding the dimensions of the matrices.

Statistical Pattern recognition(1)

The document summarizes statistical pattern recognition techniques. It is divided into 9 sections that cover topics like dimensionality reduction, classifiers, classifier combination, and unsupervised classification. The goal of pattern recognition is supervised or unsupervised classification of patterns based on features. Dimensionality reduction aims to reduce the number of features to address the curse of dimensionality when samples are limited. Multiple classifiers can be combined through techniques like stacking, bagging, and boosting. Unsupervised classification uses clustering algorithms to construct decision boundaries without labeled training data.

Perspective projection

This presentation discusses different types of 3D projections, including parallel and perspective projections. It defines projection as mapping a 3D object onto a 2D plane using projection lines. Parallel projection uses parallel lines and preserves size, while perspective projection uses converging lines and preserves realistic proportions but not exact sizes. Perspective projection can be classified into one, two, or three vanishing point types depending on how many principal axes the projection plane intersects. Examples like Leonardo da Vinci's "The Last Supper" are used to illustrate perspective projection principles.

★Mean shift a_robust_approach_to_feature_space_analysis

The document discusses the mean shift algorithm, a non-parametric technique for analyzing complex multimodal feature spaces and estimating the stationary points (modes) of the underlying probability density function without explicitly estimating it. It provides an intuitive description of mean shift using a distribution of billiard balls, and outlines how mean shift uses kernel density estimation to perform gradient ascent and converge at the densest regions, allowing it to be used for tasks like mode detection, clustering, and image segmentation.

Introduction to Graph neural networks @ Vienna Deep Learning meetup

Graphs are useful data structures that can be used to model various sorts of data: from molecular protein structures to social networks, pandemic spreading models, and visually rich content such as websites & invoices. In the recent few years, graph neural networks have done a huge leap forward. It is a powerful tool that every data scientist should know. In this talk, we will review their basic structure, show some example usages, and explore the existing (python) tools.

Implementing Neural Networks Using VLSI for Image Processing (compression)

Biological systems process the analog signals such as image and sound efficiently. To process the information the way biological systems do we make use of ANN. (Artificial Neural Networks) The focus of this paper is to review the implementation of the neural network architecture using analog components like Gilbert cell multiplier, differential amplifier for neuron activation function and tan sigmoid function circuit using MOS transistor. The neural architecture is trained using Back propagation algorithm for compressing the image. This paper surveys the methods of implementing the neural network using VLSI .Different CMOS technologies are used for implementing the circuits for arithmetic operations (i.e. 180nm, 45nm, 32nm).And the MOS transistors are working in sub threshold region. In this paper a review is made on how the VLSI architecture is used to implement neural networks and trained for compressing the image.

Graph Neural Network for Phenotype Prediction

This document describes a study on using graph neural networks (GNNs) for phenotype prediction from gene expression data. The objectives are to determine if including network information can improve predictions, which network types work best, and if GNNs can learn network inferences. It provides background on GNNs and how they generalize convolutional layers to graph data. The authors implemented a GNN model from previous work as a starting point and tested it on different network types to see which network information is most useful for predictions. Their methodology involves comparing GNN performance to other methods like random forests using 10-fold cross validation.

Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis

Cancer of the brain is deadly and requires careful surgical segmentation. The brain tumors were segmented using U-Net using a Convolutional Neural Network (CNN). When looking for overlaps of necrotic, edematous, growing, and healthy tissue, it might be hard to get relevant information from the images. The 2D U-Net network was improved and trained with the BraTS datasets to find these four areas. U-Net can set up many encoder and decoder routes that can be used to get information from images that can be used in different ways. To reduce computational time, we use image segmentation to exclude insignificant background details. Experiments on the BraTS datasets show that our proposed model for segmenting brain tumors from MRI (MRI) works well. In this study, we demonstrate that the BraTS datasets for 2017, 2018, 2019, and 2020 do not significantly differ from the BraTS 2019 dataset's attained dice scores of 0.8717 (necrotic), 0.9506 (edema), and 0.9427 (enhancing).

Generative adversarial network and its applications to speech signal and natu...

Generative adversarial network (GAN) is a new idea for training models, in which a generator and a discriminator compete against each other to improve the generation quality. Recently, GAN has shown amazing results in image generation, and a large amount and a wide variety of new ideas, techniques, and applications have been developed based on it. Although there are only few successful cases, GAN has great potential to be applied to text and speech generations to overcome limitations in the conventional methods.
There are three parts in this tutorial. In the first part, we will give an introduction of generative adversarial network (GAN) and provide a thorough review about this technology. In the second part, we will focus on the applications of GAN to speech signal processing, including speech enhancement, voice conversion, speech synthesis, and the applications of domain adversarial training to speaker recognition and lip reading. In the third part, we will describe the major challenge of sentence generation by GAN and review a series of approaches dealing with the challenge. Meanwhile, we will present algorithms that use GAN to achieve text style transformation, machine translation and abstractive summarization without paired data.

Visualizing Data Using t-SNE

t-SNE is a modern visualization algorithm that presents high-dimensional data in 2 or 3 dimensions according to some desired distances. If you have some data and you can measure their pairwise differences, t-SNE visualization can help you identify various clusters.

Graph theory

Basic Graph Theory for Under Graduate level.Easy explanation of all beginner level topic regarding graph theory. I think it will help

02 linear algebra

The document summarizes key concepts from chapter 2 of the lecture slides on linear algebra for deep learning. It defines scalars as single numbers and vectors as 1-D arrays of numbers that can be indexed. Matrices are 2-D arrays of numbers that are indexed with two numbers. Tensors generalize this to arrays with more dimensions. The document also discusses matrix operations like transpose, dot product, and inversion which are important for solving systems of linear equations. It introduces norms as functions to measure the size of vectors.

Unit 2: All

Graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects. A graph consists of vertices and edges connecting pairs of vertices. There are many types of graphs including trees, which are connected acyclic graphs. Spanning trees are subgraphs of a graph that connect all vertices using the minimum number of edges. Key concepts in graph theory include paths, connectedness, cycles, and isomorphism between graphs.

GRPHICS01 - Introduction to 3D Graphics

This is a course on the theoretical underpinnings of 3D Graphics in computing, suitable for students with a suitable grounding in technical computing.

Clipping

The document discusses different concepts related to clipping in computer graphics including 2D and 3D clipping. It describes how clipping is used to eliminate portions of objects that fall outside the viewing frustum or clip window. Various clipping techniques are covered such as point clipping, line clipping, polygon clipping, and the Cohen-Sutherland algorithm for 2D region clipping. The key purposes of clipping are to avoid drawing objects that are not visible, improve efficiency by culling invisible geometry, and prevent degenerate cases.

Translation, Dilation, Rotation, ReflectionTutorials Online

In these slides you will learn the concepts and the basics of Translation, Reflection, Dilation, and Rotation.
http://www.winpossible.com/lessons/Geometry_Translation,_Reflection,_Dilation,_and_Rotation.html

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K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...

This K-Means clustering algorithm presentation will take you through the machine learning introduction, types of clustering algorithms, k-means clustering, how does K-Means clustering work and at least explains K-Means clustering by taking a real life use case. This Machine Learning algorithm tutorial video is ideal for beginners to learn how K-Means clustering work.
Below topics are covered in this K-Means Clustering Algorithm presentation:
1. Types of Machine Learning?
2. What is K-Means Clustering?
3. Applications of K-Means Clustering
4. Common distance measure
5. How does K-Means Clustering work?
6. K-Means Clustering Algorithm
7. Demo: k-Means Clustering
8. Use case: Color compression
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
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Computer vision - edge detection

This document summarizes a lecture on edge detection in computer vision. It discusses how edges can be detected by finding places of rapid change in image intensity using derivatives and gradient filters like the Sobel operator. It also covers non-maximum suppression to thin edges and Canny edge detection, which uses two thresholds to link edges after filtering the image with derivatives of Gaussians. The Canny edge detector is presented as the standard computer vision pipeline for edge detection.

5 geometric-modeling-ppt-university-of-victoria

1) Geometric modeling is a fundamental CAD technique that represents objects using points, lines, curves, surfaces or solids.
2) Early techniques included wireframe and surface modeling but they were ambiguous and lacked topological data.
3) Solid modeling techniques like CSG and B-Rep overcome these issues by representing objects unambiguously using their volume and topology.
4) Feature-based modeling further advanced CAD by modeling objects parametrically using high-level features like holes and rounds.

Graph Representation Learning

Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. However, traditionally machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph. In this talk I will discuss methods that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. I will provide a conceptual review of key advancements in this area of representation learning on graphs, including random-walk based algorithms, and graph convolutional networks.

Linear Regression Analysis | Linear Regression in Python | Machine Learning A...

This Linear Regression in Machine Learning Presentation will help you understand the basics of Linear Regression algorithm - what is Linear Regression, why is it needed and how Simple Linear Regression works with solved examples, Linear regression analysis, applications of Linear Regression and Multiple Linear Regression model. At the end, we will implement a use case on profit estimation of companies using Linear Regression in Python. This Machine Learning presentation is ideal for beginners who want to understand Data Science algorithms as well as Machine Learning algorithms.
Below topics are covered in this Linear Regression Machine Learning Tutorial:
1. Introduction to Machine Learning
2. Machine Learning Algorithms
3. Applications of Linear Regression
4. Understanding Linear Regression
5. Multiple Linear Regression
6. Use case - Profit estimation of companies
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
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About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
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Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
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Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
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Who should take this Machine Learning Training Course?
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
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Matrix and its operations

Matrices can be added, subtracted, and multiplied according to certain rules.
- Matrices can only be added or subtracted if they are the same size. The sum or difference of matrices A and B yields a matrix C of the same size.
- Matrices can be multiplied by a scalar. Multiplying a matrix A by a scalar k results in a new matrix kA where each element is multiplied by k.
- Matrix multiplication allows combining information from two matrices but has specific rules regarding the dimensions of the matrices.

Statistical Pattern recognition(1)

The document summarizes statistical pattern recognition techniques. It is divided into 9 sections that cover topics like dimensionality reduction, classifiers, classifier combination, and unsupervised classification. The goal of pattern recognition is supervised or unsupervised classification of patterns based on features. Dimensionality reduction aims to reduce the number of features to address the curse of dimensionality when samples are limited. Multiple classifiers can be combined through techniques like stacking, bagging, and boosting. Unsupervised classification uses clustering algorithms to construct decision boundaries without labeled training data.

Perspective projection

This presentation discusses different types of 3D projections, including parallel and perspective projections. It defines projection as mapping a 3D object onto a 2D plane using projection lines. Parallel projection uses parallel lines and preserves size, while perspective projection uses converging lines and preserves realistic proportions but not exact sizes. Perspective projection can be classified into one, two, or three vanishing point types depending on how many principal axes the projection plane intersects. Examples like Leonardo da Vinci's "The Last Supper" are used to illustrate perspective projection principles.

★Mean shift a_robust_approach_to_feature_space_analysis

The document discusses the mean shift algorithm, a non-parametric technique for analyzing complex multimodal feature spaces and estimating the stationary points (modes) of the underlying probability density function without explicitly estimating it. It provides an intuitive description of mean shift using a distribution of billiard balls, and outlines how mean shift uses kernel density estimation to perform gradient ascent and converge at the densest regions, allowing it to be used for tasks like mode detection, clustering, and image segmentation.

Introduction to Graph neural networks @ Vienna Deep Learning meetup

Graphs are useful data structures that can be used to model various sorts of data: from molecular protein structures to social networks, pandemic spreading models, and visually rich content such as websites & invoices. In the recent few years, graph neural networks have done a huge leap forward. It is a powerful tool that every data scientist should know. In this talk, we will review their basic structure, show some example usages, and explore the existing (python) tools.

Implementing Neural Networks Using VLSI for Image Processing (compression)

Biological systems process the analog signals such as image and sound efficiently. To process the information the way biological systems do we make use of ANN. (Artificial Neural Networks) The focus of this paper is to review the implementation of the neural network architecture using analog components like Gilbert cell multiplier, differential amplifier for neuron activation function and tan sigmoid function circuit using MOS transistor. The neural architecture is trained using Back propagation algorithm for compressing the image. This paper surveys the methods of implementing the neural network using VLSI .Different CMOS technologies are used for implementing the circuits for arithmetic operations (i.e. 180nm, 45nm, 32nm).And the MOS transistors are working in sub threshold region. In this paper a review is made on how the VLSI architecture is used to implement neural networks and trained for compressing the image.

Graph Neural Network for Phenotype Prediction

This document describes a study on using graph neural networks (GNNs) for phenotype prediction from gene expression data. The objectives are to determine if including network information can improve predictions, which network types work best, and if GNNs can learn network inferences. It provides background on GNNs and how they generalize convolutional layers to graph data. The authors implemented a GNN model from previous work as a starting point and tested it on different network types to see which network information is most useful for predictions. Their methodology involves comparing GNN performance to other methods like random forests using 10-fold cross validation.

Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis

Cancer of the brain is deadly and requires careful surgical segmentation. The brain tumors were segmented using U-Net using a Convolutional Neural Network (CNN). When looking for overlaps of necrotic, edematous, growing, and healthy tissue, it might be hard to get relevant information from the images. The 2D U-Net network was improved and trained with the BraTS datasets to find these four areas. U-Net can set up many encoder and decoder routes that can be used to get information from images that can be used in different ways. To reduce computational time, we use image segmentation to exclude insignificant background details. Experiments on the BraTS datasets show that our proposed model for segmenting brain tumors from MRI (MRI) works well. In this study, we demonstrate that the BraTS datasets for 2017, 2018, 2019, and 2020 do not significantly differ from the BraTS 2019 dataset's attained dice scores of 0.8717 (necrotic), 0.9506 (edema), and 0.9427 (enhancing).

Generative adversarial network and its applications to speech signal and natu...

Generative adversarial network (GAN) is a new idea for training models, in which a generator and a discriminator compete against each other to improve the generation quality. Recently, GAN has shown amazing results in image generation, and a large amount and a wide variety of new ideas, techniques, and applications have been developed based on it. Although there are only few successful cases, GAN has great potential to be applied to text and speech generations to overcome limitations in the conventional methods.
There are three parts in this tutorial. In the first part, we will give an introduction of generative adversarial network (GAN) and provide a thorough review about this technology. In the second part, we will focus on the applications of GAN to speech signal processing, including speech enhancement, voice conversion, speech synthesis, and the applications of domain adversarial training to speaker recognition and lip reading. In the third part, we will describe the major challenge of sentence generation by GAN and review a series of approaches dealing with the challenge. Meanwhile, we will present algorithms that use GAN to achieve text style transformation, machine translation and abstractive summarization without paired data.

Visualizing Data Using t-SNE

t-SNE is a modern visualization algorithm that presents high-dimensional data in 2 or 3 dimensions according to some desired distances. If you have some data and you can measure their pairwise differences, t-SNE visualization can help you identify various clusters.

Graph theory

Basic Graph Theory for Under Graduate level.Easy explanation of all beginner level topic regarding graph theory. I think it will help

02 linear algebra

The document summarizes key concepts from chapter 2 of the lecture slides on linear algebra for deep learning. It defines scalars as single numbers and vectors as 1-D arrays of numbers that can be indexed. Matrices are 2-D arrays of numbers that are indexed with two numbers. Tensors generalize this to arrays with more dimensions. The document also discusses matrix operations like transpose, dot product, and inversion which are important for solving systems of linear equations. It introduces norms as functions to measure the size of vectors.

Unit 2: All

Graph theory is the study of graphs, which are mathematical structures used to model pairwise relations between objects. A graph consists of vertices and edges connecting pairs of vertices. There are many types of graphs including trees, which are connected acyclic graphs. Spanning trees are subgraphs of a graph that connect all vertices using the minimum number of edges. Key concepts in graph theory include paths, connectedness, cycles, and isomorphism between graphs.

GRPHICS01 - Introduction to 3D Graphics

This is a course on the theoretical underpinnings of 3D Graphics in computing, suitable for students with a suitable grounding in technical computing.

Clipping

The document discusses different concepts related to clipping in computer graphics including 2D and 3D clipping. It describes how clipping is used to eliminate portions of objects that fall outside the viewing frustum or clip window. Various clipping techniques are covered such as point clipping, line clipping, polygon clipping, and the Cohen-Sutherland algorithm for 2D region clipping. The key purposes of clipping are to avoid drawing objects that are not visible, improve efficiency by culling invisible geometry, and prevent degenerate cases.

K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...

K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...

Computer vision - edge detection

Computer vision - edge detection

5 geometric-modeling-ppt-university-of-victoria

5 geometric-modeling-ppt-university-of-victoria

Graph Representation Learning

Graph Representation Learning

Linear Regression Analysis | Linear Regression in Python | Machine Learning A...

Linear Regression Analysis | Linear Regression in Python | Machine Learning A...

Matrix and its operations

Matrix and its operations

Statistical Pattern recognition(1)

Statistical Pattern recognition(1)

Perspective projection

Perspective projection

★Mean shift a_robust_approach_to_feature_space_analysis

★Mean shift a_robust_approach_to_feature_space_analysis

Introduction to Graph neural networks @ Vienna Deep Learning meetup

Introduction to Graph neural networks @ Vienna Deep Learning meetup

Implementing Neural Networks Using VLSI for Image Processing (compression)

Implementing Neural Networks Using VLSI for Image Processing (compression)

Graph Neural Network for Phenotype Prediction

Graph Neural Network for Phenotype Prediction

Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis

Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis

Generative adversarial network and its applications to speech signal and natu...

Generative adversarial network and its applications to speech signal and natu...

Visualizing Data Using t-SNE

Visualizing Data Using t-SNE

Graph theory

Graph theory

02 linear algebra

02 linear algebra

Unit 2: All

Unit 2: All

GRPHICS01 - Introduction to 3D Graphics

GRPHICS01 - Introduction to 3D Graphics

Clipping

Clipping

Translation, Dilation, Rotation, ReflectionTutorials Online

In these slides you will learn the concepts and the basics of Translation, Reflection, Dilation, and Rotation.
http://www.winpossible.com/lessons/Geometry_Translation,_Reflection,_Dilation,_and_Rotation.html

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Translations, rotations, reflections, and dilations

This document discusses different types of geometric transformations including translations, rotations, reflections, and dilations. Translations move a figure across a plane without changing its size. Rotations turn a figure around a point or line. Reflections flip a figure across a line to create a mirror image. Dilation changes the size of a figure by enlarging or reducing it using a scale factor, while keeping the shape intact. The document provides examples and definitions of each transformation type.

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Oliviamath problem

100 dyas

Maths activity

The document provides the 100m race times for two classes and asks five questions about analyzing and comparing the results between the classes. It lists the individual times for each student in each class, and gives the answers to the five questions, including: the average time for each class, which class was slower/faster, the range of times for each class, and the mode time for each class.

3002 a more with parrallel lines and anglesupdated 10 22-13

1. If x = 1 and y = 2008, the value of 1/x + 1/y is 105.85.
2. The document provides instructions for homework to be placed on the corner of a desk. It also contains objectives and a two-column proof regarding parallel lines cut by a transversal.

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Proving quads are parralelograms

The document contains notes from a geometry drill on identifying parallelograms and determining values of x and y in parallelogram figures. It lists homework answers and a classwork assignment to identify parallelograms from figures and state the relevant definition or theorem, as well as an assignment to complete 15 problems showing work.

Linear approximations and_differentials

The document discusses linear approximations and differentials. It explains that a linear approximation uses the tangent line at a point to approximate nearby values of a function. The linearization of a function f at a point a is the linear function L(x) = f(a) + f'(a)(x - a). Several examples are provided of finding the linearization of functions and using it to approximate values. Differentials are also introduced, where dy represents the change along the tangent line and ∆y represents the actual change in the function.

Olivia’s math problem2

100 day of school

2d 3d animation and Digital services from Vinformax and Creantt

This document summarizes the services of a creative company called Aviformax. It has production hubs in Stockholm, the US, UK and India. The company provides creative and visual design, pre-production, production and post-production services. It helps clients with 2D and 3D motion graphics, product visualization, visual branding and digital signage solutions. The document highlights the advantages of digital signage for branding, finance, operations and technical aspects. It also describes the company's content management system and data analytics dashboard tools.

Math project

1) The document provides steps to find the coordinates of the circumcenter of a triangle with vertices A(-4,0), B(2,6), and C(8,-4).
2) It finds the equations of the perpendicular bisectors of each side by calculating the midpoints and slopes to get the equations.
3) The intersections of the three perpendicular bisectors are calculated to find the circumcenter, which is determined to be (2.5,-0.5).

Congruent figures 2013

The document provides information about congruent triangles:
- Two triangles are congruent if their corresponding sides are congruent and they have the same shape and size.
- Examples are provided to demonstrate using properties of congruent triangles to find missing angle measures and prove triangles are congruent by showing corresponding parts are equal.
- One example proves two triangles are congruent by showing bisectors of angles bisect the opposite sides, making corresponding parts congruent.

114333628 irisan-kerucut

The document discusses properties of parabolas, including their definition as the set of points equidistant from a focus point and directrix line. It presents the standard equation for a par

Power series

A power series is an infinite series of the form Σcixi or Σci(x-a)i, where the cis are constants. It represents a "polynomial" with infinitely many terms that can be used to expand functions. Common power series include the Taylor series expansions of exponential, logarithmic, and other important functions. Power series are very useful for certain mathematical calculations.

Deductivereasoning and bicond and algebraic proofs

1. The document discusses biconditional statements, conditional statements, and using deductive reasoning in geometry. It provides examples of identifying conditionals within biconditionals, writing definitions as biconditionals, and solving equations with justification in both algebra and geometry.
2. Key concepts covered include using properties of equality to write algebraic proofs, properties of congruence corresponding to properties of equality, and identifying properties of equality and congruence that justify statements.
3. Examples are provided of solving equations algebraically and geometrically with justification for each step, identifying conditionals within biconditionals, and writing definitions as biconditionals.

Symmetry,rotation, reflection,translation

The document discusses different types of symmetry including lines of symmetry, reflection, rotation, and translation. It provides examples of these symmetries using shapes like hearts, flags, polygons and math symbols. Regular polygons are noted to have multiple lines of symmetry and there is a pattern to how many lines different regular polygons will have.

Local linear approximation

The document discusses local linear approximations, which provide a linear function that closely approximates a given non-linear function near a specific point. It defines the local linear approximation at a point x0 as f(x0) + f'(x0)(x - x0). Graphs and examples are provided to illustrate how the local linear approximation can be used to estimate function values close to x0. The concept of differentials is also introduced to estimate small changes in a function using its derivative. Examples demonstrate using differentials to approximate changes and estimate errors in computations involving measured values.

Graphing inverse functions

Graphing inverse functions

Translation, Dilation, Rotation, ReflectionTutorials Online

Translation, Dilation, Rotation, ReflectionTutorials Online

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Translations, rotations, reflections, and dilations

Translations, rotations, reflections, and dilations

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Oliviamath problem

Oliviamath problem

Maths activity

Maths activity

3002 a more with parrallel lines and anglesupdated 10 22-13

3002 a more with parrallel lines and anglesupdated 10 22-13

사설토토 ＜＆＆＞∃‰∩kid85⊇∬△ ＜＆＆＞사설토토 사설토토

사설토토 ＜＆＆＞∃‰∩kid85⊇∬△ ＜＆＆＞사설토토 사설토토

Proving quads are parralelograms

Proving quads are parralelograms

Linear approximations and_differentials

Linear approximations and_differentials

Olivia’s math problem2

Olivia’s math problem2

2d 3d animation and Digital services from Vinformax and Creantt

2d 3d animation and Digital services from Vinformax and Creantt

Math project

Math project

Congruent figures 2013

Congruent figures 2013

114333628 irisan-kerucut

114333628 irisan-kerucut

Power series

Power series

Deductivereasoning and bicond and algebraic proofs

Deductivereasoning and bicond and algebraic proofs

Symmetry,rotation, reflection,translation

Symmetry,rotation, reflection,translation

Local linear approximation

Local linear approximation

Graphing inverse functions

Graphing inverse functions

How to use Firebase Data Connect For Flutter

This is how to use data connect in flutter.

June Patch Tuesday

Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.

Building Production Ready Search Pipelines with Spark and Milvus

Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.

20240609 QFM020 Irresponsible AI Reading List May 2024

Everything I found interesting about the irresponsible use of machine intelligence in May 2024

Mind map of terminologies used in context of Generative AI

Mind map of common terms used in context of Generative AI.

Uni Systems Copilot event_05062024_C.Vlachos.pdf

Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems

GenAI Pilot Implementation in the organizations

GenAI Pilot Implementation

Removing Uninteresting Bytes in Software Fuzzing

Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.

Artificial Intelligence for XMLDevelopment

In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.

HCL Notes and Domino License Cost Reduction in the World of DLAU

Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away

Presentation of the OECD Artificial Intelligence Review of Germany

Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm

UiPath Test Automation using UiPath Test Suite series, part 6

Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP

National Security Agency - NSA mobile device best practices

Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.

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ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。

Full-RAG: A modern architecture for hyper-personalization

Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.

AI 101: An Introduction to the Basics and Impact of Artificial Intelligence

Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.

Columbus Data & Analytics Wednesdays - June 2024

Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20

How to Get CNIC Information System with Paksim Ga.pptx

Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.

Essentials of Automations: The Art of Triggers and Actions in FME

In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!

“I’m still / I’m still / Chaining from the Block”

“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.

How to use Firebase Data Connect For Flutter

How to use Firebase Data Connect For Flutter

June Patch Tuesday

June Patch Tuesday

Building Production Ready Search Pipelines with Spark and Milvus

Building Production Ready Search Pipelines with Spark and Milvus

20240609 QFM020 Irresponsible AI Reading List May 2024

20240609 QFM020 Irresponsible AI Reading List May 2024

Mind map of terminologies used in context of Generative AI

Mind map of terminologies used in context of Generative AI

Uni Systems Copilot event_05062024_C.Vlachos.pdf

Uni Systems Copilot event_05062024_C.Vlachos.pdf

GenAI Pilot Implementation in the organizations

GenAI Pilot Implementation in the organizations

Removing Uninteresting Bytes in Software Fuzzing

Removing Uninteresting Bytes in Software Fuzzing

Artificial Intelligence for XMLDevelopment

Artificial Intelligence for XMLDevelopment

HCL Notes and Domino License Cost Reduction in the World of DLAU

HCL Notes and Domino License Cost Reduction in the World of DLAU

Presentation of the OECD Artificial Intelligence Review of Germany

Presentation of the OECD Artificial Intelligence Review of Germany

UiPath Test Automation using UiPath Test Suite series, part 6

UiPath Test Automation using UiPath Test Suite series, part 6

National Security Agency - NSA mobile device best practices

National Security Agency - NSA mobile device best practices

みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...

みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...

Full-RAG: A modern architecture for hyper-personalization

Full-RAG: A modern architecture for hyper-personalization

AI 101: An Introduction to the Basics and Impact of Artificial Intelligence

AI 101: An Introduction to the Basics and Impact of Artificial Intelligence

Columbus Data & Analytics Wednesdays - June 2024

Columbus Data & Analytics Wednesdays - June 2024

How to Get CNIC Information System with Paksim Ga.pptx

How to Get CNIC Information System with Paksim Ga.pptx

Essentials of Automations: The Art of Triggers and Actions in FME

Essentials of Automations: The Art of Triggers and Actions in FME

“I’m still / I’m still / Chaining from the Block”

“I’m still / I’m still / Chaining from the Block”

- 1. The Minkowski sum (applied to 2d geometry) cloderic.mars@gmail.com http://www.crowdscontrol.net clodericmars
- 2. Formal deﬁnition A and B are two sets A⊕B is the Minkowski sum of A and B A⊕B = {a+b! a∈A, b∈B}
- 3. What if A and B are 2D shapes ? Hard to visualize ? Let’s see some examples...
- 4. Example 1 A is any polygon B is a convex polygon
- 5. A B x y
- 6. A⊕B x y
- 7. Example 2 A is any polygon B is any disc
- 8. A B x y
- 9. A⊕B x y
- 10. Intuitive deﬁnition What is A⊕B ? Take B Dip it into some paint Put its (0,0) on A border Translate it along the A perimeter The painted area is A⊕B
- 11. What can you do with that ? Notably, motion planning
- 12. Free space A is an obstacle any 2D polygon B is a moving object 2D translation : t shape : a convex polygon or a disc t ∈ A⊕-B collision
- 13. Example 1 A is any polygon B is a convex polygon
- 14. A B x y -B
- 15. A⊕-B x y
- 16. x y t t ∉ A⊕-B no collision
- 17. x y t t ∈ A⊕-B collision
- 18. Example 2 A is any polygon B is any disc
- 19. A B=-B x y
- 20. A⊕-B x y
- 21. A⊕-B x y t t ∉ A⊕-B no collision
- 22. A⊕-B x y t t ∈ A⊕-B collision
- 23. How is it computed ?
- 24. Two convex polygons ConvexPolygon minkowskiSum(ConvexPolygon a, ConvexPolygon b) { Vertex[] computedVertices; foreach(Vertex vA in a) { foreach(Vertex vB in b) { computedVertices.push_back(vA+vB); } } return convexHull(computedVertices); }
- 25. Any polygons Method 1 : decomposition decompose in convex polygons compute the sum of each couple the ﬁnal sum is the union of each sub-sum Method 2 : convolution cf. sources
- 26. Polygon offsetting P is a polygon D is a disc of radius r Computing P⊕D = Offsetting P by a radius r Computation Easy for a convex polygon cf. sources