Feature learning - PowerPoint PPT Presentation


Graph Machine Learning Overview: Traditional ML to Graph Neural Networks

Explore the evolution of Machine Learning in Graphs, from traditional ML tasks to advanced Graph Neural Networks (GNNs). Discover key concepts like feature engineering, tools like PyG, and types of ML tasks in graphs. Uncover insights into node-level, graph-level, and community-level predictions, an

3 views • 87 slides


Feature-Based Agile Product Roadmap Template

Keep track of all planned product features with this comprehensive roadmap. Instantly gain insight into each feature and its duration, allowing for customization to timebox sprint periods for Agile development. Ideal for PI planning to prioritize key features effectively.

2 views • 6 slides



Modeling Scientific Software Architecture for Feature Readiness

This work discusses the importance of understanding software architecture in assessing the readiness of user-facing features in scientific software. It explores the challenges of testing complex features, presents a motivating example, and emphasizes the role of subject matter experts in validating

4 views • 20 slides


Update Summary of S-123 Data Model Revision and Major Changes

This document outlines the revisions and major changes in the S-123 Data Model, including the addition and remodeling of feature types, information types, and data models to support remote control and connectivity. It also details the removal and addition of attributes and the restructuring of compl

5 views • 23 slides


Parameter and Feature Recommendations for NBA-UWB MMS Operations

This document presents recommendations for parameter and feature sets to enhance the NBA-UWB MMS operations, focusing on lowering testing costs and enabling smoother interoperations. Key aspects covered include interference mitigation techniques, coexistence improvements, enhanced ranging capabiliti

3 views • 18 slides


Advanced Machine Learning: Data Preparation and Exploration Part 1

This lecture on advanced machine learning covers topics such as the ML process in detail, data understanding, sources, types, exploration, preparation, scaling, feature selection, data balancing, and more. The ML process involves steps like defining the problem, preparing data, selecting and evaluat

0 views • 80 slides


What is Adjusted Service Date in QuickBooks?

What is Adjusted Service Date in QuickBooks?\nQuickBooks offers a powerful feature called the Adjusted Service Date, which reflects the actual date a service was provided. This is crucial for accurate financial reporting, better cash flow management, and maintaining strong customer relations. Unlike

0 views • 7 slides


Best service for Feature Walls in Carrigoon Beg

Derek McNamara Joinery serves the Best service for Feature Walls in Carrigoon Beg. They prides itself on delivering superior results that exceed customer expectations. They are skilled in a wide range of carpentry, such as panelling, feature walls, radiator covers, furniture design, and using only t

1 views • 6 slides


Progress Update on S-124 Development and Approval Process

The content provides a detailed agenda for a meeting in Monaco, discussing the progress and development story of S-124, including the approval of Edition 1.0.0. It covers key topics such as the Feature Catalog, Guidance Documentation, Validation, and implementing S-124/S-412 into the GMDSS. The deve

0 views • 18 slides


Exploring a Cutting-Edge Convolutional Neural Network for Speech Emotion Recognition

Human speech is a rich source of emotional indicators, making Speech Emotion Recognition (SER) vital for intelligent systems to understand emotions. SER involves extracting emotional states from speech and categorizing them. This process includes feature extraction and classification, utilizing tech

1 views • 15 slides


Git Branching Models and Workflows

Git branching models determine how code changes are managed and integrated in software development projects. This content discusses successful branching models, emphasizing the usage of master, develop, feature, release, and hotfix branches. It also explains why Git branching is different from centr

0 views • 13 slides


A Unified Approach to Interpreting Model Predictions

Unified methodology for interpreting model predictions through additive explanations and Shapley values. It discusses the relationship between Additive Explanations and LIME, introduces Shapley values, approximations, experiments, and extensions in model interpretation. The approach unifies various

1 views • 21 slides


Understanding Constructive Solid Geometry Concepts

Explore Constructive Solid Geometry (CSG) concepts including binary tree representation, Boolean operations, base feature selection in parametric modeling, and the importance of order in feature creation. Learn about CSG's role in representing solid models and its applications in the field of solid

1 views • 13 slides


Exporting STATA Results to Excel Using PutExcel Feature

Learn how to utilize the PutExcel feature in STATA to effortlessly export your results to an Excel file. With PutExcel, you can export matrices, stored results, images, estimation tables, and even add formulas for calculations. This tool streamlines the process of transferring statistical data to Ex

3 views • 32 slides


Understanding Learning Intentions and Success Criteria

Learning intentions and success criteria play a crucial role in enhancing student focus, motivation, and responsibility for their learning. Research indicates that students benefit greatly from having clear learning objectives and criteria for success. Effective learning intentions should identify w

1 views • 24 slides


Proposal to Add National Security and Emergency Preparedness Priority Access Feature in IEEE 802.11be Amendment

The document proposes integrating the National Security and Emergency Preparedness (NSEP) priority access feature into the IEEE 802.11be standard to ensure seamless NSEP service experience, particularly in Wi-Fi networks used as last-mile access. The NSEP priority feature at the MAC layer is indepen

0 views • 12 slides


Promote Feature Adoption with Self-Service Password Reset Posters

Enhance feature adoption of self-service password reset among your employees with these ready-to-use posters. Simply customize and print them to encourage password security awareness in your workplace. Don't risk productivity downtime due to forgotten passwords – empower your team to reset their p

0 views • 13 slides


Unleashing the Power of Feature Stories in Writing

Feature stories offer a unique way to engage readers by focusing on personal elements and timeless themes compared to the timeliness of news reports. They allow for creativity, entertainment, and emotion, broadening the storytelling landscape. Understanding the distinction between news reports and f

0 views • 9 slides


Experiential Learning Portfolio Program at Barry University

Experiential Learning Portfolio Program at Barry University's School of Professional and Career Education (PACE) offers a unique opportunity to earn college credit for learning gained from work and community service experiences. Through this program, students can showcase their experiential learning

0 views • 16 slides


Efficient Gradient Boosting with LightGBM

Gradient Boosting Decision Tree (GBDT) is a powerful machine learning algorithm known for its efficiency and accuracy. However, handling big data poses challenges due to time-consuming computations. LightGBM introduces optimizations like Gradient-based One-Side Sampling (GOSS) and Exclusive Feature

0 views • 13 slides


Understanding Feature Engineering in Machine Learning

Feature engineering involves transforming raw data into meaningful features to improve the performance of machine learning models. This process includes selecting, iterating, and improving features, converting context to input for learning algorithms, and balancing the complexity of features, concep

0 views • 28 slides


CSEP 546 Machine Learning Course Overview

This course, led by Geoff Hulten and TAs Alon Milchgrub and Andrew Wei, delves into important machine learning algorithms and model production techniques. Topics covered include logistic regression, feature engineering, decision trees, intelligent user experiences, computer vision basics, neural net

0 views • 10 slides


Seminar on Machine Learning with IoT Explained

Explore the intersection of Machine Learning and Internet of Things (IoT) in this informative seminar. Discover the principles, advantages, and applications of Machine Learning algorithms in the context of IoT technology. Learn about the evolution of Machine Learning, the concept of Internet of Thin

0 views • 21 slides


Global Relevance and Redundancy Optimization in Multi-label Feature Selection

The study focuses on optimizing multi-label feature selection by balancing global relevance and redundancy factors, aiming to enhance the efficiency and accuracy of data analysis. It delves into the challenges posed by information theoretical-based methods and offers insights on overcoming limitatio

0 views • 15 slides


Parallel Chi-square Test for Feature Selection in Categorical Data

The chi-square test is a popular method for feature selection in categorical data with classification labels. By calculating chi-square values in parallel for all features simultaneously, this approach provides a more efficient solution compared to serial computation. The process involves creating c

1 views • 4 slides


Understanding the Difference Between News and Feature Photography

Differentiating between news and feature photography involves capturing specific events for news photos and unique cultural moments or human interest stories for feature photos. News photos inform viewers with concrete information, while feature photos evoke emotions and delve into a slice of life o

0 views • 24 slides


SolidWorks Lofted Boss/Base Feature Tutorial

Learn to use the Lofted Boss/Base feature in SolidWorks to create basic or complex 3D models such as a flowerpot. Explore how to work with planes, add new planes, and sketch the contour of parts to utilize this powerful tool effectively.

0 views • 15 slides


Innovative Learning Management System - LAMS at Belgrade Metropolitan University

Belgrade Metropolitan University (BMU) utilizes the Learning Activity Management System (LAMS) to enhance the learning process by integrating learning objects with various activities. This system allows for complex learning processes, mixing learning objects with LAMS activities effectively. The pro

4 views • 16 slides


Holland Brook School Spring Concert 2017 Featuring Strings, Chorus, and Bands

Holland Brook School is hosting its annual Spring Concert on June 8, 2017, at 7:00 PM. The concert will feature performances by the Strings, Chorus, and Bands, showcasing talented students across different grades. Directed by Mr. Jack Hasselbring, the event will include delightful musical pieces ran

1 views • 14 slides


Understanding the Importance of Feature Engineering in Data Science

Feature engineering, a manual and time-consuming process, is a crucial step in data science workflows. It involves generating and transforming features based on domain knowledge. Avoiding the pitfalls of past technologies like expert systems, feature selection plays a key role in determining which f

0 views • 25 slides


Covert Visual Search and Effective Oculomotor Range Constraints

The study delves into whether covert visual search is biologically limited by the Effective Oculomotor Range (EOMR), exploring neuropsychological evidence, eye movement studies, and participant measurements. It investigates the impact on visual search tasks, including color, orientation, and conjunc

1 views • 15 slides


Parallel Implementations of Chi-Square Test for Feature Selection

The chi-square test is an effective method for feature selection with categorical data and classification labels. It helps rank features based on their chi-square values or p-values, indicating importance. Parallel processing techniques, such as GPU implementation in CUDA, can significantly speed up

0 views • 4 slides


Advanced Techniques in Relational Data Outlier Detection

This document delves into cutting-edge methods for outlier detection in relational data, focusing on profile-based and model-based approaches such as leveraging Bayesian networks, feature generation, and individual feature vector summarization. The examples provided showcase the application of these

1 views • 30 slides


Modelling and Optimization of Quality Attributes in Software Variability

Modelling and multi-objective optimization of quality attributes in variability-rich software is crucial for customizing software functionality to meet stakeholders' diverse needs. This involves addressing conflicting quality requirements such as cost, reliability, performance, and binary footprint

0 views • 34 slides


Enhancing Iris Recognition with Circular Contourlet Transform

Iris recognition is a reliable biometric identification method due to the iris's unique properties. By incorporating the Circular Contourlet Transform (CCT) into the classical iris recognition algorithm, the feature extraction process can be enhanced to improve recognition rates under unconstrained

0 views • 14 slides


Feature Writing: A Narrative Journey Through Unique Characters and Stories

Dive into the world of feature writing, where journalistic articles take on a narrative approach to captivate readers. Explore the lives of individuals like Miley, Amy, Natasia, and Monica, each with their own compelling stories and experiences. From Harry Potter fans to Japanese tea party organizer

0 views • 51 slides


Asian American Feature Films and Mira Nair's Mississippi Masala

Explore the realm of Asian American feature films with a focus on the acclaimed movie "Mississippi Masala" directed by Mira Nair. Delve into themes of South Asian diaspora, interracial relations, stereotypes, and realism portrayed in this cinematic work alongside insights into Nair's career and cont

0 views • 24 slides


Analysis of Deep Learning Models for EEG Data Processing

This content delves into the application of deep learning models, such as Sequential Modeler, Feature Extraction, and Discriminator, for processing EEG data from the TUH EEG Corpus. The architecture involves various layers like Convolution, Max Pooling, ReLU activation, and Dropout. It explores temp

0 views • 15 slides


Pairwise Feature Learning for Unseen Plant Disease Recognition

Plant diseases pose a significant threat to agricultural production, often caused by pathogenic organisms. This project focuses on pairwise feature learning for the recognition of unseen plant diseases. The research aims to design a model that can effectively classify both seen and unseen compositio

0 views • 11 slides


Revisiting Semantic Feature Analysis: A Classic Therapy Technique

Aphasia often involves semantic breakdown, and Semantic Feature Analysis (SFA) is a foundational technique for various treatments addressing semantic impairments. This presentation explores the effectiveness of SFA in improving naming, generalization to spontaneous speech, and treatment goals beyond

0 views • 16 slides