This document summarizes a machine learning workshop on feature selection. It discusses typical feature selection methods like single feature evaluation using metrics like mutual information and Gini indexing. It also covers subset selection techniques like sequential forward selection and sequential backward selection. Examples are provided showing how feature selection improves performance for logistic regression on large datasets with more features than samples. The document outlines the workshop agenda and provides details on when and why feature selection is important for machine learning models.
Logistic regression in Machine LearningKuppusamy P
Logistic regression is a predictive analysis algorithm that can be used for classification problems. It estimates the probabilities of different classes using the logistic function, which outputs values between 0 and 1. Logistic regression transforms its output using the sigmoid function to return a probability value. It is used for problems like email spam detection, fraud detection, and tumor classification. The independent variables should be independent of each other and the dependent variable must be categorical. Gradient descent is used to minimize the loss function and optimize the model parameters during training.
Introduction to linear regression and the maths behind it like line of best fit, regression matrics. Other concepts include cost function, gradient descent, overfitting and underfitting, r squared.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
The document discusses hyperparameters and hyperparameter tuning in deep learning models. It defines hyperparameters as parameters that govern how the model parameters (weights and biases) are determined during training, in contrast to model parameters which are learned from the training data. Important hyperparameters include the learning rate, number of layers and units, and activation functions. The goal of training is for the model to perform optimally on unseen test data. Model selection, such as through cross-validation, is used to select the optimal hyperparameters. Training, validation, and test sets are also discussed, with the validation set used for model selection and the test set providing an unbiased evaluation of the fully trained model.
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
The document discusses gradient descent methods for unconstrained convex optimization problems. It introduces gradient descent as an iterative method to find the minimum of a differentiable function by taking steps proportional to the negative gradient. It describes the basic gradient descent update rule and discusses convergence conditions such as Lipschitz continuity, strong convexity, and condition number. It also covers techniques like exact line search, backtracking line search, coordinate descent, and steepest descent methods.
This document summarizes a machine learning workshop on feature selection. It discusses typical feature selection methods like single feature evaluation using metrics like mutual information and Gini indexing. It also covers subset selection techniques like sequential forward selection and sequential backward selection. Examples are provided showing how feature selection improves performance for logistic regression on large datasets with more features than samples. The document outlines the workshop agenda and provides details on when and why feature selection is important for machine learning models.
Logistic regression in Machine LearningKuppusamy P
Logistic regression is a predictive analysis algorithm that can be used for classification problems. It estimates the probabilities of different classes using the logistic function, which outputs values between 0 and 1. Logistic regression transforms its output using the sigmoid function to return a probability value. It is used for problems like email spam detection, fraud detection, and tumor classification. The independent variables should be independent of each other and the dependent variable must be categorical. Gradient descent is used to minimize the loss function and optimize the model parameters during training.
Introduction to linear regression and the maths behind it like line of best fit, regression matrics. Other concepts include cost function, gradient descent, overfitting and underfitting, r squared.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
The document discusses hyperparameters and hyperparameter tuning in deep learning models. It defines hyperparameters as parameters that govern how the model parameters (weights and biases) are determined during training, in contrast to model parameters which are learned from the training data. Important hyperparameters include the learning rate, number of layers and units, and activation functions. The goal of training is for the model to perform optimally on unseen test data. Model selection, such as through cross-validation, is used to select the optimal hyperparameters. Training, validation, and test sets are also discussed, with the validation set used for model selection and the test set providing an unbiased evaluation of the fully trained model.
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
The document discusses gradient descent methods for unconstrained convex optimization problems. It introduces gradient descent as an iterative method to find the minimum of a differentiable function by taking steps proportional to the negative gradient. It describes the basic gradient descent update rule and discusses convergence conditions such as Lipschitz continuity, strong convexity, and condition number. It also covers techniques like exact line search, backtracking line search, coordinate descent, and steepest descent methods.
Linear regression is a supervised machine learning technique used to model the relationship between a continuous dependent variable and one or more independent variables. It is commonly used for prediction and forecasting. The regression line represents the best fit line for the data using the least squares method to minimize the distance between the observed data points and the regression line. R-squared measures how well the regression line represents the data, on a scale of 0-100%. Linear regression performs well when data is linearly separable but has limitations such as assuming linear relationships and being sensitive to outliers and multicollinearity.
The document discusses artificial neural networks and backpropagation. It provides an overview of backpropagation algorithms, including how they were developed over time, the basic methodology of propagating errors backwards, and typical network architectures. It also gives examples of applying backpropagation to problems like robotics, space robots, handwritten digit recognition, and face recognition.
Logistic regression is a machine learning classification algorithm that predicts the probability of a categorical dependent variable. It models the probability of the dependent variable being in one of two possible categories, as a function of the independent variables. The model transforms the linear combination of the independent variables using the logistic sigmoid function to output a probability between 0 and 1. Logistic regression is optimized using maximum likelihood estimation to find the coefficients that maximize the probability of the observed outcomes in the training data. Like linear regression, it makes assumptions about the data being binary classified with no noise or highly correlated independent variables.
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
This document provides an overview of decision trees, including:
- Decision trees classify records by sorting them down the tree from root to leaf node, where each leaf represents a classification outcome.
- Trees are constructed top-down by selecting the most informative attribute to split on at each node, usually based on information gain.
- Trees can handle both numerical and categorical data and produce classification rules from paths in the tree.
- Examples of decision tree algorithms like ID3 that use information gain to select the best splitting attribute are described. The concepts of entropy and information gain are defined for selecting splits.
Abstract: This PDSG workshop introduces basic concepts of splitting a dataset for training a model in machine learning. Concepts covered are training, test and validation data, serial and random splitting, data imbalance and k-fold cross validation.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
Cross-validation is a technique used to evaluate machine learning models by reserving a portion of a dataset to test the model trained on the remaining data. There are several common cross-validation methods, including the test set method (reserving 30% of data for testing), leave-one-out cross-validation (training on all data points except one, then testing on the left out point), and k-fold cross-validation (randomly splitting data into k groups, with k-1 used for training and the remaining group for testing). The document provides an example comparing linear regression, quadratic regression, and point-to-point connection on a concrete strength dataset using k-fold cross-validation. SPSS output for the
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
Linear regression with gradient descentSuraj Parmar
Intro to the very popular optimization Technique(Gradient descent) with linear regression . Linear regression with Gradient descent on www.landofai.com
This document provides an overview of linear regression techniques including:
- Single dimension linear regression which finds the best fitting line to predict a target variable y based on a single input variable x.
- Multi-dimension linear regression which extends this to multiple input variables by finding the best fitting hyperplane. Gradient descent can be used to minimize error.
- Polynomial regression can be performed by including powers of input variables.
- One-hot encoding represents categorical variables as binary variables to work with linear models.
Decision trees are a type of supervised learning algorithm used for classification and regression. ID3 and C4.5 are algorithms that generate decision trees by choosing the attribute with the highest information gain at each step. Random forest is an ensemble method that creates multiple decision trees and aggregates their results, improving accuracy. It introduces randomness when building trees to decrease variance.
Overfitting and underfitting are modeling errors related to how well a model fits training data. Overfitting occurs when a model is too complex and fits the training data too closely, resulting in poor performance on new data. Underfitting occurs when a model is too simple and does not fit the training data well. The bias-variance tradeoff aims to balance these issues by finding a model complexity that minimizes total error.
Supervised learning and Unsupervised learning Usama Fayyaz
This document discusses supervised and unsupervised machine learning. Supervised learning uses labeled training data to learn a function that maps inputs to outputs. Unsupervised learning is used when only input data is available, with the goal of modeling underlying structures or distributions in the data. Common supervised algorithms include decision trees and logistic regression, while common unsupervised algorithms include k-means clustering and dimensionality reduction.
This document discusses and provides examples of supervised and unsupervised learning. Supervised learning involves using labeled training data to learn relationships between inputs and outputs and make predictions. An example is using data on patients' attributes to predict the likelihood of a heart attack. Unsupervised learning involves discovering hidden patterns in unlabeled data by grouping or clustering items with similar attributes, like grouping fruits by color without labels. The goal of supervised learning is to build models that can make predictions when new examples are presented.
07 logistic regression and stochastic gradient descentSubhas Kumar Ghosh
This document provides an overview of logistic regression using stochastic gradient descent. It explains that logistic regression can be used for classification problems where the output is discrete. The key aspects covered include:
- Logistic regression estimates the logit (log odds) of the probability rather than the probability directly, using a linear function of the input features.
- It learns a hyperplane that separates the classes by choosing weights to maximize the likelihood of the training data.
- Stochastic gradient descent can be used as an optimization technique to learn the weights by minimizing the negative log likelihood.
- An example is provided of using the Mahout machine learning library to build a logistic regression model for classification using features from a donut-
Linear regression is a supervised machine learning technique used to model the relationship between a continuous dependent variable and one or more independent variables. It is commonly used for prediction and forecasting. The regression line represents the best fit line for the data using the least squares method to minimize the distance between the observed data points and the regression line. R-squared measures how well the regression line represents the data, on a scale of 0-100%. Linear regression performs well when data is linearly separable but has limitations such as assuming linear relationships and being sensitive to outliers and multicollinearity.
The document discusses artificial neural networks and backpropagation. It provides an overview of backpropagation algorithms, including how they were developed over time, the basic methodology of propagating errors backwards, and typical network architectures. It also gives examples of applying backpropagation to problems like robotics, space robots, handwritten digit recognition, and face recognition.
Logistic regression is a machine learning classification algorithm that predicts the probability of a categorical dependent variable. It models the probability of the dependent variable being in one of two possible categories, as a function of the independent variables. The model transforms the linear combination of the independent variables using the logistic sigmoid function to output a probability between 0 and 1. Logistic regression is optimized using maximum likelihood estimation to find the coefficients that maximize the probability of the observed outcomes in the training data. Like linear regression, it makes assumptions about the data being binary classified with no noise or highly correlated independent variables.
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
This document provides an overview of decision trees, including:
- Decision trees classify records by sorting them down the tree from root to leaf node, where each leaf represents a classification outcome.
- Trees are constructed top-down by selecting the most informative attribute to split on at each node, usually based on information gain.
- Trees can handle both numerical and categorical data and produce classification rules from paths in the tree.
- Examples of decision tree algorithms like ID3 that use information gain to select the best splitting attribute are described. The concepts of entropy and information gain are defined for selecting splits.
Abstract: This PDSG workshop introduces basic concepts of splitting a dataset for training a model in machine learning. Concepts covered are training, test and validation data, serial and random splitting, data imbalance and k-fold cross validation.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
Cross-validation is a technique used to evaluate machine learning models by reserving a portion of a dataset to test the model trained on the remaining data. There are several common cross-validation methods, including the test set method (reserving 30% of data for testing), leave-one-out cross-validation (training on all data points except one, then testing on the left out point), and k-fold cross-validation (randomly splitting data into k groups, with k-1 used for training and the remaining group for testing). The document provides an example comparing linear regression, quadratic regression, and point-to-point connection on a concrete strength dataset using k-fold cross-validation. SPSS output for the
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
Linear regression with gradient descentSuraj Parmar
Intro to the very popular optimization Technique(Gradient descent) with linear regression . Linear regression with Gradient descent on www.landofai.com
This document provides an overview of linear regression techniques including:
- Single dimension linear regression which finds the best fitting line to predict a target variable y based on a single input variable x.
- Multi-dimension linear regression which extends this to multiple input variables by finding the best fitting hyperplane. Gradient descent can be used to minimize error.
- Polynomial regression can be performed by including powers of input variables.
- One-hot encoding represents categorical variables as binary variables to work with linear models.
Decision trees are a type of supervised learning algorithm used for classification and regression. ID3 and C4.5 are algorithms that generate decision trees by choosing the attribute with the highest information gain at each step. Random forest is an ensemble method that creates multiple decision trees and aggregates their results, improving accuracy. It introduces randomness when building trees to decrease variance.
Overfitting and underfitting are modeling errors related to how well a model fits training data. Overfitting occurs when a model is too complex and fits the training data too closely, resulting in poor performance on new data. Underfitting occurs when a model is too simple and does not fit the training data well. The bias-variance tradeoff aims to balance these issues by finding a model complexity that minimizes total error.
Supervised learning and Unsupervised learning Usama Fayyaz
This document discusses supervised and unsupervised machine learning. Supervised learning uses labeled training data to learn a function that maps inputs to outputs. Unsupervised learning is used when only input data is available, with the goal of modeling underlying structures or distributions in the data. Common supervised algorithms include decision trees and logistic regression, while common unsupervised algorithms include k-means clustering and dimensionality reduction.
This document discusses and provides examples of supervised and unsupervised learning. Supervised learning involves using labeled training data to learn relationships between inputs and outputs and make predictions. An example is using data on patients' attributes to predict the likelihood of a heart attack. Unsupervised learning involves discovering hidden patterns in unlabeled data by grouping or clustering items with similar attributes, like grouping fruits by color without labels. The goal of supervised learning is to build models that can make predictions when new examples are presented.
07 logistic regression and stochastic gradient descentSubhas Kumar Ghosh
This document provides an overview of logistic regression using stochastic gradient descent. It explains that logistic regression can be used for classification problems where the output is discrete. The key aspects covered include:
- Logistic regression estimates the logit (log odds) of the probability rather than the probability directly, using a linear function of the input features.
- It learns a hyperplane that separates the classes by choosing weights to maximize the likelihood of the training data.
- Stochastic gradient descent can be used as an optimization technique to learn the weights by minimizing the negative log likelihood.
- An example is provided of using the Mahout machine learning library to build a logistic regression model for classification using features from a donut-
Regression takes a group of random variables, thought to be predicting Y, and tries to find a mathematical relationship between them. This relationship is typically in the form of a straight line (linear regression) that best approximates all the individual data points.
Linear regression is a popular machine learning algorithm that models the linear relationship between a dependent variable and one or more independent variables. Simple linear regression uses one independent variable, while multiple linear regression uses more than one. The linear regression model finds coefficients that help predict the dependent variable based on the independent variables. The model performance is evaluated using metrics like the coefficient of determination (R-squared). Linear regression makes assumptions such as a linear relationship between variables and normally distributed errors.
Linear regression is a popular machine learning algorithm that models the linear relationship between a dependent variable and one or more independent variables. Simple linear regression uses one independent variable, while multiple linear regression uses more than one. The linear regression model finds coefficients that help predict the dependent variable based on the independent variables. The model performance is evaluated using metrics like the coefficient of determination (R-squared). Linear regression makes assumptions such as a linear relationship between variables and normally distributed errors.
Lecture 5 - Gradient Descent, a lecture in subject module Statistical & Machi...Maninda Edirisooriya
Gradient Descent is the most commonly used learning algorithm for learning, including Deep Neural Networks with Back Propagation. This was one of the lectures of a full course I taught in University of Moratuwa, Sri Lanka on 2023 second half of the year.
1. This document discusses improving linear regression models by replacing ordinary least squares with alternative fitting procedures. Two reasons for this are to improve prediction accuracy and model interpretability.
2. When the number of predictors is large compared to the number of observations, ordinary least squares estimates can have high variance and overfit the data. Regularization methods like ridge regression and the LASSO improve this by shrinking coefficients.
3. Ridge regression adds a penalty term to the least squares optimization, shrinking coefficients toward zero. The LASSO uses an alternative penalty term that can shrink some coefficients exactly to zero, performing embedded variable selection.
The document summarizes the EM algorithm and its applications to problems with missing data. It discusses:
1. The EM algorithm is an iterative method to estimate parameters in problems with missing or unknown data. It alternates between estimating the missing data given the current parameters, and re-estimating the parameters given the estimated missing data.
2. Applications discussed include line fitting with missing coordinates, and image segmentation treating pixel class assignments as missing data.
3. The algorithm is also applicable to problems with outliers by including an outlier model component. The RANSAC algorithm is an alternative approach that searches for a consensus model among random samples.
Artificial Intelligence Course: Linear models ananth
In this presentation we present the linear models: Regression and Classification. We illustrate with several examples. Concepts such as underfitting (Bias) and overfitting (Variance) are presented. Linear models can be used as stand alone classifiers for simple cases and they are essential building blocks as a part of larger deep learning networks
This document summarizes the NGBoost method for probabilistic regression. NGBoost uses gradient boosting to fit the parameters of an assumed probabilistic distribution for the target variable. It improves on existing probabilistic regression methods by using the natural gradient, which performs gradient descent in the space of distributions rather than the parameter space. This addresses issues with prior approaches and allows NGBoost to achieve state-of-the-art performance while remaining fast, flexible, and scalable. Future work may apply NGBoost to other problems like survival analysis or joint outcome regression.
- Linear regression estimates the relationship between continuous dependent and independent variables using a best fit line. Multiple linear regression uses multiple independent variables while simple linear regression uses one.
- Logistic regression applies a sigmoid function to linear regression when the dependent variable is binary. It handles non-linear relationships between variables.
- Polynomial regression uses higher powers of independent variables which may lead to overfitting so model fit must be checked.
- Stepwise regression automatically selects independent variables using forward selection or backward elimination. Ridge and lasso regression address multicollinearity through regularization. Elastic net is a hybrid of ridge and lasso.
- Classification algorithms include k-nearest neighbors, decision trees, support vector machines, and naive Bayes which use probability
Logistic regression is a machine learning classification algorithm used to predict the probability of a categorical dependent variable given one or more independent variables. It uses a logit link function to transform the probability values into odds ratios between 0 and infinity. The model is trained by minimizing a cost function called logistic loss using gradient descent optimization. Model performance is evaluated using metrics like accuracy, precision, recall, and the confusion matrix, and can be optimized by adjusting the probability threshold for classifications.
Scaling transforms data values to fall within a specific range, such as 0 to 1, without changing the data distribution. Normalization changes the data distribution to be normal. Common normalization techniques include standardization, which transforms data to have mean 0 and standard deviation 1, and Box-Cox transformation, which finds the best lambda value to make data more normal. Normalization is useful for algorithms that assume normal data distributions and can improve model performance and interpretation.
Deep learning uses multilayered neural networks to process information in a robust, generalizable, and scalable way. It has various applications including image recognition, sentiment analysis, machine translation, and more. Deep learning concepts include computational graphs, artificial neural networks, and optimization techniques like gradient descent. Prominent deep learning architectures include convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks.
Linear regression and logistic regression are two machine learning algorithms that can be implemented in Python. Linear regression is used for predictive analysis to find relationships between variables, while logistic regression is used for classification with binary dependent variables. Support vector machines (SVMs) are another algorithm that finds the optimal hyperplane to separate data points and maximize the margin between the classes. Key terms discussed include cost functions, gradient descent, confusion matrices, and ROC curves. Code examples are provided to demonstrate implementing linear regression, logistic regression, and SVM in Python using scikit-learn.
Logistic regression estimates the probability of an event occurring based on independent variables. It is used when the dependent variable is binary or categorical. The logistic function transforms the probability to a value between 0 and 1. Maximum likelihood estimation is used to find the parameter estimates that maximize the likelihood of obtaining the observed sample data.
Regression analysis is a statistical technique used to model relationships between variables. Simple regression uses one independent variable to predict a dependent variable, while multiple regression uses two or more independent variables. Both aim to find the coefficients that minimize prediction error by fitting linear equations to data. Ordinary least squares estimation determines the optimal slope and intercept coefficients by minimizing the sum of squared errors between predicted and actual values.
This document provides an overview of machine learning topics including linear regression, linear classification models, decision trees, random forests, supervised learning, unsupervised learning, reinforcement learning, and regression analysis. It defines machine learning, describes how machines learn through training, validation and application phases, and lists applications of machine learning such as risk assessment and fraud detection. It also explains key machine learning algorithms and techniques including linear regression, naive bayes, support vector machines, decision trees, gradient descent, least squares, multiple linear regression, bayesian linear regression, and types of machine learning models.
In recent years, technological advancements have reshaped human interactions and work environments. However, with rapid adoption comes new challenges and uncertainties. As we face economic challenges in 2023, business leaders seek solutions to address their pressing issues.
The Ultimate Guide to Top 36 DevOps Testing Tools for 2024.pdfkalichargn70th171
Testing is pivotal in the DevOps framework, serving as a linchpin for early bug detection and the seamless transition from code creation to deployment.
DevOps teams frequently adopt a Continuous Integration/Continuous Deployment (CI/CD) methodology to automate processes. A robust testing strategy empowers them to confidently deploy new code, backed by assurance that it has passed rigorous unit and performance tests.
Secure-by-Design Using Hardware and Software Protection for FDA ComplianceICS
This webinar explores the “secure-by-design” approach to medical device software development. During this important session, we will outline which security measures should be considered for compliance, identify technical solutions available on various hardware platforms, summarize hardware protection methods you should consider when building in security and review security software such as Trusted Execution Environments for secure storage of keys and data, and Intrusion Detection Protection Systems to monitor for threats.
Streamlining End-to-End Testing Automation with Azure DevOps Build & Release Pipelines
Automating end-to-end (e2e) test for Android and iOS native apps, and web apps, within Azure build and release pipelines, poses several challenges. This session dives into the key challenges and the repeatable solutions implemented across multiple teams at a leading Indian telecom disruptor, renowned for its affordable 4G/5G services, digital platforms, and broadband connectivity.
Challenge #1. Ensuring Test Environment Consistency: Establishing a standardized test execution environment across hundreds of Azure DevOps agents is crucial for achieving dependable testing results. This uniformity must seamlessly span from Build pipelines to various stages of the Release pipeline.
Challenge #2. Coordinated Test Execution Across Environments: Executing distinct subsets of tests using the same automation framework across diverse environments, such as the build pipeline and specific stages of the Release Pipeline, demands flexible and cohesive approaches.
Challenge #3. Testing on Linux-based Azure DevOps Agents: Conducting tests, particularly for web and native apps, on Azure DevOps Linux agents lacking browser or device connectivity presents specific challenges in attaining thorough testing coverage.
This session delves into how these challenges were addressed through:
1. Automate the setup of essential dependencies to ensure a consistent testing environment.
2. Create standardized templates for executing API tests, API workflow tests, and end-to-end tests in the Build pipeline, streamlining the testing process.
3. Implement task groups in Release pipeline stages to facilitate the execution of tests, ensuring consistency and efficiency across deployment phases.
4. Deploy browsers within Docker containers for web application testing, enhancing portability and scalability of testing environments.
5. Leverage diverse device farms dedicated to Android, iOS, and browser testing to cover a wide range of platforms and devices.
6. Integrate AI technology, such as Applitools Visual AI and Ultrafast Grid, to automate test execution and validation, improving accuracy and efficiency.
7. Utilize AI/ML-powered central test automation reporting server through platforms like reportportal.io, providing consolidated and real-time insights into test performance and issues.
These solutions not only facilitate comprehensive testing across platforms but also promote the principles of shift-left testing, enabling early feedback, implementing quality gates, and ensuring repeatability. By adopting these techniques, teams can effectively automate and execute tests, accelerating software delivery while upholding high-quality standards across Android, iOS, and web applications.
Strengthening Web Development with CommandBox 6: Seamless Transition and Scal...Ortus Solutions, Corp
Join us for a session exploring CommandBox 6’s smooth website transition and efficient deployment. CommandBox revolutionizes web development, simplifying tasks across Linux, Windows, and Mac platforms. Gain insights and practical tips to enhance your development workflow.
Come join us for an enlightening session where we delve into the smooth transition of current websites and the efficient deployment of new ones using CommandBox 6. CommandBox has revolutionized web development, consistently introducing user-friendly enhancements that catalyze progress in the field. During this presentation, we’ll explore CommandBox’s rich history and showcase its unmatched capabilities within the realm of ColdFusion, covering both major variations.
The journey of CommandBox has been one of continuous innovation, constantly pushing boundaries to simplify and optimize development processes. Regardless of whether you’re working on Linux, Windows, or Mac platforms, CommandBox empowers developers to streamline tasks with unparalleled ease.
In our session, we’ll illustrate the simple process of transitioning existing websites to CommandBox 6, highlighting its intuitive features and seamless integration. Moreover, we’ll unveil the potential for effortlessly deploying multiple websites, demonstrating CommandBox’s versatility and adaptability.
Join us on this journey through the evolution of web development, guided by the transformative power of CommandBox 6. Gain invaluable insights, practical tips, and firsthand experiences that will enhance your development workflow and embolden your projects.
How GenAI Can Improve Supplier Performance Management.pdfZycus
Data Collection and Analysis with GenAI enables organizations to gather, analyze, and visualize vast amounts of supplier data, identifying key performance indicators and trends. Predictive analytics forecast future supplier performance, mitigating risks and seizing opportunities. Supplier segmentation allows for tailored management strategies, optimizing resource allocation. Automated scorecards and reporting provide real-time insights, enhancing transparency and tracking progress. Collaboration is fostered through GenAI-powered platforms, driving continuous improvement. NLP analyzes unstructured feedback, uncovering deeper insights into supplier relationships. Simulation and scenario planning tools anticipate supply chain disruptions, supporting informed decision-making. Integration with existing systems enhances data accuracy and consistency. McKinsey estimates GenAI could deliver $2.6 trillion to $4.4 trillion in economic benefits annually across industries, revolutionizing procurement processes and delivering significant ROI.
Digital Marketing Introduction and ConclusionStaff AgentAI
Digital marketing encompasses all marketing efforts that utilize electronic devices or the internet. It includes various strategies and channels to connect with prospective customers online and influence their decisions. Key components of digital marketing include.
2. Agenda
• Single Dimension Linear Regression
• Multi Dimension Linear Regression
• Gradient Descent
• Generalisation, Over-fitting & Regularisation
• Categorical Inputs
3. What is Linear Regression?
• Learning
• A supervised algorithm that learns from a set of training samples.
• Each training sample has one or more input values and a single output value.
• The algorithm learns the line, plane or hyper-plane that best fits the training
samples.
• Prediction
• Use the learned line, plane or hyper-plane to predict the output value for any
input sample.
5. Single Dimension Linear Regression
• Single dimension linear regression
has pairs of x and y values as input
training samples.
• It uses these training sample to
derive a line that predicts values of y.
• The training samples are used to
derive the values of a and b that
minimise the error between actual
and predicated values of y.
6. Single Dimension Linear Regression
• We want a line that minimises the
error between the Y values in
training samples and the Y values
that the line passes through.
• Or put another way, we want the
line that “best fits’ the training
samples.
• So we define the error function for
our algorithm so we can minimise
that error.
7. Single Dimension Linear Regression
• To determine the value of a that
minimises the error E, we look for
where the partial differential of E
with respect to a is zero.
8. Single Dimension Linear Regression
• To determine the value of b that
minimises the error E, we look for
where the partial differential of E
with respect to b is zero.
9. Single Dimension Linear Regression
• By substituting the final equations
from the previous two slides we
derive equations for a and b that
minimise the error
10. Single Dimension Linear Regression
• We also define a function which we can
use to score how well derived line fits.
• A value of 1 indicates a perfect fit.
• A value of 0 indicates a fit that is no
better than simply predicting the mean
of the input y values.
• A negative value indicates a fit that is
even worse than just predicting the
mean of the input y values.
15. Multi Dimension Linear Regression
• Each training sample has an x made
up of multiple input values and a
corresponding y with a single value.
• The inputs can be represented as
an X matrix in which each row is
sample and each column is a
dimension.
• The outputs can be represented as
y matrix in which each row is a
sample.
16. Multi Dimension Linear Regression
• Our predicated y values are
calculated by multiple the X matrix
by a matrix of weights, w.
• If there are 2 dimension, then this
equation defines plane. If there are
more dimensions then it defines a
hyper-plane.
17. Multi Dimension Linear Regression
• We want a plane or hyper-plane
that minimises the error between
the y values in training samples
and the y values that the plane or
hyper-plane passes through.
• Or put another way, we want the
plane/hyper-plane that “best fits’
the training samples.
• So we define the error function for
our algorithm so we can minimise
that error.
18. Multi Dimension Linear Regression
• To determine the value of w that
minimises the error E, we look for
where the differential of E with
respect to w is zero.
• We use the Matrix Cookbook to
help with the differentiation!
19. Multi Dimension Linear Regression
• We also define a function which we can
use to score how well derived line fits.
• A value of 1 indicates a perfect fit.
• A value of 0 indicates a fit that is no
better than simply predicting the mean
of the input y values.
• A negative value indicates a fit that is
even worse than just predicting the
mean of the input y values.
22. Multi Dimension Linear Regression
• In addition to using the X matrix to represent basic features our training
data, we can can also introduce additional dimensions (i.e. columns in
our X matrix) that are derived from those basic feature values.
• If we introduce derived features whose values are powers of basic
features, our multi-dimensional linear regression can then derive
polynomial curves, planes and hyper-planes.
23. Multi Dimension Linear Regression
• For example, if we have just one
basic feature in each sample of X, we
can include a range of powers of that
value into our X matrix like this:
• In non-matrix form our multi-
dimensional linear equation is:
• Inserting the powers of the basic
feature that we have introduced this
becomes a polynomial:
27. Singular Matrices
• As we have seen, we can use
numpy’s linalg.solve() function to
determine the value of the weights
that result in the lowest possible error.
• But this doesn’t work if np.dot(X.T, X)
is a singular matrix.
• It results in the matrix equivalent of a
divide by zero.
• Gradient descent is an alternative
approach to determining the optimal
weights that in works for all cases,
including this singular matrix case.
28. Gradient Descent
• Gradient descent is a technique we can use to find the minimum of
arbitrarily complex error functions.
• In gradient descent we pick a random set of weights for our algorithm and
iteratively adjust those weights in the direction of the gradient of the error
with respect to each weight.
• As we iterate, the gradient approaches zero and we approach the
minimum error.
• In machine learning we often use gradient descent with our error function
to find the weights that give the lowest errors.
29. Gradient Descent
• Here is an example with a very
simple function:
• The gradient of this function is
given by:
• We choose an random initial
value for x and a learning rate of
0.1 and then start descent.
• On each iteration our x value is
decreasing and the gradient (2x)
is converging towards 0.
30. Gradient Descent
• The learning rate is a what is know as a hyper-parameter.
• If the learning rate is too small then convergence may take a very long
time.
• If the learning rate is too large then convergence may never happen
because our iterations bounce from one side of the minima to the other.
• Choosing a suitable value for hyper-parameters is an art so try different
values and plot the results until you find suitable values.
31. Multi Dimension Linear Regression
with Gradient Descent
• For multi dimension linear
regression our error function
is:
• Differentiating this with
respect to the weights vector
gives:
• We can iteratively reduce the
error by adjusting the weights
in the direction of these
gradients.
35. Generalisation & Over-fitting
• As we train our model with more and more data the it may start to fit the training data more and
more accurately, but become worse at handling test data that we feed to it later.
• This is know as “over-fitting” and results in an increased generalisation error.
• To minimise the generalisation error we should
• Collect as much sample data as possible.
• Use a random subset of our sample data for training.
• Use the remaining sample data to test how well our model copes with data it was not trained
with.
• Also, experiment with adding higher degrees of polynomials (X2, X3, etc) as this can reduce
overfitting.
36. L1 Regularisation (Lasso)
• Having a large number of samples (n) with respect to the number of
dimensionality (d) increases the quality of our model.
• One way to reduce the effective number of dimensions is to use those that
most contribute to the signal and ignore those that mostly act as noise.
• L1 regularisation achieves this by adding a penalty that results in the
weight for the dimensions that act as noise becoming 0.
• L1 regularisation encourages a sparse vector of weights in which few are
non-zero and many are zero.
37. L1 Regularisation (Lasso)
• In L1 regularisation we add a penalty to
the error function:
• Expanding this we get:
• Take the derivative with respect to w to
find our gradient:
• Where sign(w) is -1 if w < 0, 0 if w = 0
and +1 if w > 0
• Note that because sign(w) has no
inverse function we cannot solve for w
and so must use gradient descent.
40. L2 Regularisation (Ridge)
• Another way to reduce the complexity of our model and prevent overfitting
to outliers is L2 regression, which is also known as ridge regression.
• In L2 Regularisation we introduce an additional term to the cost function
that has the effect of penalising large weights and thereby minimising this
skew.
41. L2 Regularisation (Ridge)
• In L2 regularisation we the sum of
the squares of the weights to the
error function.
• Expanding this we get:
• Take the derivative with respect to
w to find our gradient:
45. L1 & L2 Regularisation (Elastic Net)
• L1 Regularisation minimises the impact of dimensions that have low
weights and are thus largely “noise”.
• L2 Regularisation minimise the impacts of outliers in our training data.
• L1 & L2 Regularisation can be used together and the combination is
referred to as Elastic Net regularisation.
• Because the differential of the error function contains the sigmoid which
has no inverse, we cannot solve for w and must use gradient descent.
47. One-hot Encoding
• When some inputs are categories (e.g. gender) rather than numbers (e.g.
age) we need to represent the category values as numbers so they can be
used in our linear regression equations.
• In one-hot encoding we allocate each category value it's own dimension in
the inputs. So, for example, we allocate X1 to Audi, X2 to BMW & X3 to
Mercedes.
• For Audi X = [1,0,0]
• For BMW X = [0,1,0])
• For Mercedes X = [0,0,1]
48. Summary
• Single Dimension Linear Regression
• Multi Dimension Linear Regression
• Gradient Descent
• Generalisation, Over-fitting & Regularisation
• Categorical Inputs
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