Heartbeat

SEPTEMBER 7, 2023

In the ever-evolving landscape of machine learning and artificial intelligence, understanding and explaining the decisions made by models have become paramount. Enter Comet , that streamlines the model development process and strongly emphasizes model interpretability and explainability. Why Does It Matter?

Explainability 52

Explainability Machine Learning Data Scientist Deep Learning 52

Pickl AI

AUGUST 28, 2024

Unlike regression, which deals with continuous output variables, classification involves predicting categorical output variables. They are easy to interpret and can handle both categorical and numerical data. Understand the unique characteristics and challenges of each type to apply the right approach effectively.

Machine Learning 52

Machine Learning Algorithm Categorization Neural Network 52

Pickl AI

SEPTEMBER 17, 2024

Explain The Concept of Supervised and Unsupervised Learning. It involves tasks such as handling missing values, removing outliers, encoding categorical variables, and scaling numerical features. Explain The Concept of Overfitting and Underfitting In Machine Learning Models.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Neural Network Machine Learning 52

Marktechpost

DECEMBER 11, 2024

Models were categorized into three groups: real-world use cases, long-context processing, and general domain tasks. To ensure safe and responsible use of the models, LG AI Research verified the open-source libraries employed and committed to monitoring AI regulations across different jurisdictions. The safety of EXAONE 3.5

AI Researcher 82

AI Researcher AI Research Generative AI AI 82

Viso.ai

DECEMBER 6, 2023

Image Classification Image classification tasks involve CV models categorizing images into user-defined classes for various applications. Based on the presence of a tiger, the entire image is categorized as such. Semantic Segmentation Semantic segmentation aims to identify each pixel within an image for a more detailed categorization.

Computer Vision 52

Computer Vision Convolutional Neural Networks Neural Network Categorization 52

DataRobot Blog

DECEMBER 20, 2022

In this educated example , the aim is to predict home prices at the property level in the city of Madrid and the training dataset contains 5 different data types (numerical, categorical, text, location, and images) and +90 variables that are related to these 5 different groups: Market performance. Global Explainability.

Explainability 59

Explainability Automation Machine Learning AI 59

Viso.ai

APRIL 25, 2024

It’s important to note that the categorization of visual dataset bias can vary between sources. However, it is worth noting that even though this class imbalance has a significant impact, they do not explain every disparity in the performance of machine learning algorithms. This section will use the framework outlined here.

Computer Vision 52

Computer Vision Categorization Algorithm Deep Learning 52

Heartbeat

FEBRUARY 2, 2024

COCO-QA: Shifting attention to COCO-QA, questions are categorized based on types such as color, counting, location, and object. This categorization lays the groundwork for nuanced evaluation, recognizing that different question types demand distinct reasoning strategies from VQA algorithms. In xxAI — Beyond Explainable AI Chapter.

Computer Vision 52

Computer Vision Natural Language Processing Artificial Intelligence Artificial Intelligence 52

Pickl AI

NOVEMBER 18, 2024

After cleaning, the data may need to be preprocessed, which includes scaling numerical features, encoding categorical variables, and transforming text or images into formats suitable for the model. converting dates into day of the week, creating dummy variables for categorical data). Let’s explore some of the key trends.

Machine Learning 52

Machine Learning Algorithm Data Quality Neural Network 52

Pickl AI

JULY 1, 2024

Decision Trees These tree-like structures categorize data and predict demand based on a series of sequential decisions. Explainable AI (XAI) As models become more complex, XAI techniques will be crucial for understanding how models arrive at their predictions, fostering trust and interpretability in the forecasting process.

Data Science 52

Data Science Neural Network Machine Learning Categorization 52

Mlearning.ai

MARCH 24, 2023

The EU AI Act is a proposed piece of legislation that seeks to regulate the development and deployment of artificial intelligence (AI) systems across the European Union. Photo by Guillaume Périgois on Unsplash EU AI Act: History and Timeline 2018 : EU Commission starts pilot project on ‘Explainable AI’.

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Categorization Explainability 52

IBM Journey to AI blog

FEBRUARY 9, 2024

They make AI more explainable: the larger the model, the more difficult it is to pinpoint how and where it makes important decisions. Explainable AI is essential to understanding, improving and trusting the output of AI systems.

AI 298

AI AI Artificial Intelligence Artificial Intelligence 298