Heartbeat

NOVEMBER 14, 2023

For many years, gradient-boosting models and deep-learning solutions have won the lion's share of Kaggle competitions. XGBoost is not limited to machine learning tasks, as its incredible power can be harnessed when harmonized with deep learning algorithms. " Nuclear Engineering and Technology 53, no.

Deep Learning 52

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Artificial Corner

JULY 30, 2023

In this world of complex terminologies, someone who wants to explain Large Language Models (LLMs) to some non-tech guy is a difficult task. So that’s why I tried in this article to explain LLM in simple or to say general language. Machine translation, summarization, ticket categorization, and spell-checking are among the examples.

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Heartbeat

FEBRUARY 21, 2024

Photo by Almos Bechtold on Unsplash Deep learning is a machine learning sub-branch that can automatically learn and understand complex tasks using artificial neural networks. Deep learning uses deep (multilayer) neural networks to process large amounts of data and learn highly abstract patterns.

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Viso.ai

MARCH 26, 2024

The introduction of the Transformer model was a significant leap forward for the concept of attention in deep learning. Furthermore, attention mechanisms work to enhance the explainability or interpretability of AI models. Vaswani et al. without conventional neural networks.

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

Marktechpost

SEPTEMBER 26, 2023

Many graphical models are designed to work exclusively with continuous or categorical variables, limiting their applicability to data that spans different types. Moreover, specific restrictions, such as continuous variables not being allowed as parents of categorical variables in directed acyclic graphs (DAGs), can hinder their flexibility.

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Heartbeat

FEBRUARY 22, 2023

Deep learning for feature extraction, ensemble models, and more Photo by DeepMind on Unsplash The advent of deep learning has been a game-changer in machine learning, paving the way for the creation of complex models capable of feats previously thought impossible.

Deep Learning 52

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IBM Journey to AI blog

JULY 17, 2023

Currently chat bots are relying on rule-based systems or traditional machine learning algorithms (or models) to automate tasks and provide predefined responses to customer inquiries. is a studio to train, validate, tune and deploy machine learning (ML) and foundation models for Generative AI. Watsonx.ai

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Mlearning.ai

JANUARY 26, 2023

When it comes to implementing any ML model, the most difficult question asked is how do you explain it. Suppose, you are a data scientist working closely with stakeholders or customers, even explaining the model performance and feature selection of a Deep learning model is quite a task. How do we deal with this?

Explainability 52

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IBM Journey to AI blog

AUGUST 1, 2023

It’s the underlying engine that gives generative models the enhanced reasoning and deep learning capabilities that traditional machine learning models lack. Visit the watsonx webpage to learn more The post How foundation models and data stores unlock the business potential of generative AI appeared first on IBM Blog.

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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?

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Marktechpost

OCTOBER 18, 2023

The recent results of machine learning in drug discovery have been largely attributed to graph and geometric deep learning models. Like other deep learning techniques, they need a lot of training data to provide excellent modeling accuracy.

Machine Learning 127

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IBM Journey to AI blog

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. Also, conversational AI systems can manage and categorize support tickets, prioritizing them based on urgency and relevance. Ensuring fairness and inclusivity in conversational AI is crucial.

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NVIDIA

NOVEMBER 8, 2023

207 While an AI designed for categorizing traffic lights, for example, doesn’t need perfection, medical tools must be highly accurate — any oversight could be fatal. Currently, Annalise.ai works for chest X-rays and brain CT scans, with more on the way. The AI Podcast · Harrison.ai To overcome this challenge, annalise.ai

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NVIDIA

NOVEMBER 8, 2023

207 While an AI designed for categorizing traffic lights, for example, doesn’t need perfection, medical tools must be highly accurate — any oversight could be fatal. Currently, Annalise.ai works for chest X-rays and brain CT scans, with more on the way. The AI Podcast · Harrison.ai To overcome this challenge, annalise.ai

Categorization 86

Categorization Deep Learning AI Tools Machine Learning 86

PyImageSearch

SEPTEMBER 25, 2023

In this lesson, we will answer this question by explaining the machine learning behind YouTube video recommendations. To address all these challenges, YouTube employs a two-stage deep learning-based recommendation strategy that trains large-scale models (with approximately one billion parameters) on hundreds of billions of examples.

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Computer Vision Deep Learning Neural Network Algorithm 111

Flipboard

MARCH 7, 2023

Contact Lens for Amazon Connect generates call and chat transcripts; derives contact summary, analytics, categorization of associate-customer interaction, and issue detection; and measures customer sentiments. Contact Lens rules help us categorize known issues in the contact center.

ML 123

ML Deep Learning Algorithm Categorization 123

Explosion

NOVEMBER 9, 2016

This post explains the components of this new approach, and shows how they’re put together in two recent systems. now features deep learning models for named entity recognition, dependency parsing, text classification and similarity prediction based on the architectures described in this post. Here’s how to do that.

Deep Learning 52

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PyImageSearch

NOVEMBER 13, 2023

Home Table of Contents Faster R-CNNs Object Detection and Deep Learning Measuring Object Detector Performance From Where Do the Ground-Truth Examples Come? One of the most popular deep learning-based object detection algorithms is the family of R-CNN algorithms, originally introduced by Girshick et al.

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PyImageSearch

FEBRUARY 6, 2023

Furthermore, this tutorial aims to develop an image classification model that can learn to classify one of the 15 vegetables (e.g., If you are a regular PyImageSearch reader and have even basic knowledge of Deep Learning in Computer Vision, then this tutorial should be easy to understand. tomato, brinjal, and bottle gourd).

Auto-classification 131

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Towards AI

JUNE 4, 2024

Accelerating Transformers with NVIDIA cuDNN 9 The NVIDIA cuDNN is a GPU-accelerated library for accelerating deep learning primitives with state-of-the-art performance. This article explains linear regression in the context of spatial analysis and shows a practical example of its use in GIS.

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IBM Journey to AI blog

APRIL 18, 2024

Most experts categorize it as a powerful, but narrow AI model. Building an in-house team with AI, deep learning , machine learning (ML) and data science skills is a strategic move. Some, like Goertzel and Pennachin , suggest that AGI would possess self-understanding and self-control.

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AWS Machine Learning Blog

JANUARY 17, 2023

In addition to textual inputs, this model uses traditional structured data inputs such as numerical and categorical fields. We show you how to train, deploy and use a churn prediction model that has processed numerical, categorical, and textual features to make its prediction. For more details, refer to the GitHub repo.

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NVIDIA

JULY 5, 2023

Magic co-founder, CEO and AI lead Eric Steinberger explained how his company is trying to build an AGI AI software engineer that will work as though it were a team of humans. Rather than creating an alternative to existing solutions, Magic sees itself as trying to build something categorically different.

Generative AI 98

Generative AI Software Engineer Explainability Categorization 98

IBM Journey to AI blog

OCTOBER 16, 2023

For instance, email management automation tools such as Levity use ML to identify and categorize emails as they come in using text classification algorithms. Reinforcement learning uses ML to train models to identify and respond to cyberattacks and detect intrusions. The platform has three powerful components: the watsonx.ai

Machine Learning 131

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Marktechpost

FEBRUARY 23, 2025

Resources from DigitalOcean and GitHub help us categorize these agents based on their capabilities and operational approaches. Decision Engines: At the core of an AI agent is its decision engine, which uses a blend of machine learning models, statistical algorithms, and rule-based logic to choose appropriate actions.

Natural Language Processing 56

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Viso.ai

DECEMBER 18, 2023

As an Edge AI implementation, TensorFlow Lite greatly reduces the barriers to introducing large-scale computer vision with on-device machine learning, making it possible to run machine learning everywhere. TensorFlow Lite is an open-source deep learning framework designed for on-device inference ( Edge Computing ).

Computer Vision 98

Computer Vision Machine Learning Deep Learning Neural Network 98

Pickl AI

AUGUST 28, 2024

Classification is a supervised learning technique where the model predicts the category or class that a new observation belongs to, based on the patterns learned from the training data. Unlike regression, which deals with continuous output variables, classification involves predicting categorical output variables.

Machine Learning 52

Machine Learning Algorithm Categorization Neural Network 52

Marktechpost

FEBRUARY 17, 2025

Papers were annotated with metadata such as author affiliations, publication year, and citation count and were categorized based on methodological approaches, specific safety concerns addressed, and risk mitigation strategies. Recent efforts emphasize reinforcement learning safety, adversarial robustness, and explainability.

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Towards AI

JULY 22, 2023

We’ll start with a simple explainer of how Machine Learning models work — let’s say you want to predict how late your upcoming flight’s arrival time will be. At the end of the article, you will hopefully walk away with a fuller picture of this rapidly evolving topic. Let’s dive in. A very basic version can be human guess work (eg.

Large Language Models 97

Large Language Models ChatGPT OpenAI Neural Network 97

Heartbeat

JUNE 27, 2023

These tasks require the model to categorize edge types or predict the existence of an edge between two given nodes. In these tasks, the model must learn comprehensive graph representations. Want to get the most up-to-date news on all things Deep Learning? GNNs also differ in their graph execution process.

Neural Network 59

Neural Network Convolutional Neural Networks Auto-classification Deep Learning 59

Marktechpost

JULY 4, 2023

A model’s parameters are the components learned from previous training data and, in essence, establish the model’s proficiency on a task, such as text generation. Natural language processing (NLP) activities, including speech-to-text, sentiment analysis, text summarization, spell-checking, token categorization, etc.,

Large Language Models 104

Large Language Models BERT Natural Language Processing Categorization 104

Towards AI

FEBRUARY 13, 2023

A practical guide on how to perform NLP tasks with Hugging Face Pipelines Image by Canva With the libraries developed recently, it has become easier to perform deep learning analysis. Let me explain. Zero-Shot Classification Imagine you want to categorize unlabeled text. One of these libraries is Hugging Face.

Auto-classification 98

Auto-classification NLP Auto-complete Computer Vision 98

Mlearning.ai

MARCH 31, 2023

In this story, we talk about how to build a Deep Learning Object Detector from scratch using TensorFlow. The output layer is set to use Softmax Activation Function as usual in Deep Learning classifiers. That time, tensorflow/pytorch and the Deep Learning technology were not ready yet.

Deep Learning 98

Deep Learning Machine Learning Python Categorization 98

Pickl AI

SEPTEMBER 17, 2024

What Is the Difference Between Artificial Intelligence, Machine Learning, And Deep Learning? Artificial Intelligence (AI) is a broad field that encompasses the development of systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.

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Heartbeat

JANUARY 24, 2024

The importance of EDA in the machine learning world is well known to its users. Making visualizations is one of the finest ways for data scientists to explain data analysis to people outside the business. We do the univariate analysis of numerical and categorical variables differently.

Data Analysis 52

Data Analysis Categorization Data Scientist Machine Learning 52

Heartbeat

JANUARY 10, 2024

Its crucial capability is processing categorical data without converting it to numerical data. This means that the model can perform its function as you desire after specifying categorical data. Catboost requires a user to specify the categorical features that a dataset has. We pay our contributors, and we don’t sell ads.

Categorization 52

Categorization Deep Learning Data Science Data Scientist 52

Marktechpost

JULY 17, 2023

What Relationship Exists Between Predictive Analytics, Deep Learning, and Artificial Intelligence? For machine learning to identify common patterns, large datasets must be processed. Deep learning is a branch of machine learning frequently used with text, audio, visual, or photographic data.

Machine Learning 98

Machine Learning Data Mining Data Scientist Data Science 98