Marktechpost

DECEMBER 19, 2023

In tackling the intricate task of predicting brain age, researchers introduce a groundbreaking hybrid deep learning model that integrates Convolutional Neural Networks (CNN) and Multilayer Perceptron (MLP) architectures. If you like our work, you will love our newsletter.

Convolutional Neural Networks 115

Convolutional Neural Networks Neural Network Algorithm AI Researcher 115

Marktechpost

JANUARY 14, 2024

There has been a meteoric rise in the use of deep learning in image processing in the past several years. The robust feature learning and mapping capabilities of deep learning-based approaches enable them to acquire intricate blur removal patterns from large datasets.

Deep Learning 92

Deep Learning Convolutional Neural Networks Neural Network Computer Vision 92

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.

Neural Network 128

Neural Network Categorization Data Scientist Data Analysis 128

Heartbeat

AUGUST 21, 2023

In this guide, we’ll talk about Convolutional Neural Networks, how to train a CNN, what applications CNNs can be used for, and best practices for using CNNs. What Are Convolutional Neural Networks CNN? CNNs are artificial neural networks built to handle data having a grid-like architecture, such as photos or movies.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Natural Language Processing Computer Vision 52

The MLOps Blog

MARCH 1, 2024

TL;DR Deep learning models exhibit excellent performance but require high computational resources. Deep learning models continue to dominate the machine-learning landscape. Training and operating the deep learning models is expensive and time-consuming and has a significant impact on the environment.

Deep Learning 59

Deep Learning Neural Network Categorization Machine Learning 59

Heartbeat

OCTOBER 11, 2023

This process is known as machine learning or deep learning. Two of the most well-known subfields of AI are machine learning and deep learning. Supervised, unsupervised, and reinforcement learning : Machine learning can be categorized into different types based on the learning approach.

Deep Learning 52

Deep Learning Machine Learning Neural Network Natural Language Processing 52

Viso.ai

JANUARY 2, 2024

In the following, we will explore Convolutional Neural Networks (CNNs), a key element in computer vision and image processing. Whether you’re a beginner or an experienced practitioner, this guide will provide insights into the mechanics of artificial neural networks and their applications. Howard et al.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Computer Vision Data Scarcity 52

Marktechpost

NOVEMBER 16, 2023

A new research paper presents a deep learning-based classifier for age-related macular degeneration (AMD) stages using retinal optical coherence tomography (OCT) scans. The model, trained on a substantial dataset, performs strongly in categorizing macula-centered 3D volumes into Normal, iAMD, GA, and nAMD stages.

Deep Learning 88

Deep Learning Convolutional Neural Networks Neural Network Categorization 88

Marktechpost

NOVEMBER 26, 2023

The capacity of a model to use inputs at inference time to modify its behavior without updating its weights to tackle problems that were not present during training is known as in-context learning or ICL. Due to these convincing discoveries, emergent capabilities in massive neural networks have been the subject of study.

Neural Network 123

Neural Network Categorization AI Researcher AI Research 123

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.

Deep Learning 52

Deep Learning Neural Network Algorithm Machine Learning 52

Heartbeat

AUGUST 29, 2023

Deep learning is a branch of machine learning that makes use of neural networks with numerous layers to discover intricate data patterns. Deep learning models use artificial neural networks to learn from data. Deep learning models use artificial neural networks to learn from data.

Software Development 52

Software Development Deep Learning Neural Network Convolutional Neural Networks 52

Marktechpost

MARCH 11, 2024

Value functions are a core component of deep reinforcement learning (RL). Value functions, implemented with neural networks, undergo training via mean squared error regression to align with bootstrapped target values. These methods aim to improve robustness and scalability in deep RL.

Neural Network 104

Neural Network Categorization Deep Learning Robotics 104

Mlearning.ai

MAY 8, 2023

A comprehensive step-by-step guide with data analysis, deep learning, and regularization techniques Introduction In this article, we will use different deep-learning TensorFlow neural networks to evaluate their performances in detecting whether cell nuclei mass from breast imaging is malignant or benign.

Neural Network 52

Neural Network Deep Learning Machine Learning Data Analysis 52

ODSC - Open Data Science

APRIL 28, 2023

In this post, I’ll be demonstrating two deep learning approaches to sentiment analysis. Deep learning refers to the use of neural network architectures, characterized by their multi-layer design (i.e. deep” architecture). These can be customized and trained. We’ll be mainly using the “.cats”

Deep Learning 98

Deep Learning Convolutional Neural Networks Neural Network NLP 98

Analytics Vidhya

JUNE 26, 2023

Introduction Attention models, also known as attention mechanisms, are input processing techniques used in neural networks. They allow the network to focus on different aspects of complex input individually until the entire data set is categorized.

Neural Network 235

Neural Network Categorization Deep Learning NLP 235

Unite.AI

JULY 21, 2023

Fundamentally, an image recognition algorithm generally uses machine learning & deep learning models to identify objects by analyzing every individual pixel in an image. Training the Neural Networks on the Dataset Once the images have been labeled, they will be fed to the neural networks for training on the images.

Computer Vision 244

Computer Vision Convolutional Neural Networks Neural Network Deep Learning 244

Viso.ai

MARCH 26, 2024

The introduction of the Transformer model was a significant leap forward for the concept of attention in deep learning. Uniquely, this model did not rely on conventional neural network architectures like convolutional or recurrent layers. without conventional neural networks. Vaswani et al.

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

Heartbeat

MARCH 1, 2023

Tracking your image classification experiments with Comet ML Photo from nmedia on Shutterstock.com Introduction Image classification is a task that involves training a neural network to recognize and classify items in images. A dataset of labeled images is used to train the network, with each image given a particular class or label.

Convolutional Neural Networks 52

Convolutional Neural Networks Neural Network Auto-classification Categorization 52

Marktechpost

DECEMBER 22, 2023

Researchers from Lebanese American University and United Arab Emirates University have collaborated to employ artificial intelligence (AI) successfully through the Scale Conjugate Gradient Neural Network (SCJGNN), providing numerical solutions for the language-based learning model.

Deep Learning 56

Deep Learning Categorization Neural Network Artificial Intelligence 56

Heartbeat

JANUARY 12, 2023

How to Log Your Keras Deep Learning Experiments With Comet Image by rawpixel.com on Freepik Overview Let us start by asking ourselves some questions: Have you ever wondered how Google’s translation app can instantly convert entire paragraphs between two languages? What is Deep Learning? Experience is the best teacher.

Deep Learning 52

Deep Learning Convolutional Neural Networks Neural Network Machine Learning 52

Viso.ai

DECEMBER 19, 2023

Today, the use of convolutional neural networks (CNN) is the state-of-the-art method for image classification. Image Classification Using Machine Learning CNN Image Classification (Deep Learning) Example applications of Image Classification Let’s dive deep into it! How Does Image Classification Work?

Convolutional Neural Networks 98

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 98

Unite.AI

MAY 29, 2024

With nine times the speed of the Nvidia A100, these GPUs excel in handling deep learning workloads. Named Entity Recognition ( NER) Named entity recognition (NER), an NLP technique, identifies and categorizes key information in text. The encoder processes input data, condensing essential features into a “Context Vector.”

Data Extraction 147

Data Extraction Neural Network Large Language Models Automation 147

John Snow Labs

JUNE 2, 2023

Sentiment analysis, also known as opinion mining, is the process of computationally identifying and categorizing the subjective information contained in natural language text. Deep learning models can automatically learn features and representations from raw text data, making them well-suited for sentiment analysis tasks.

Deep Learning 98

Deep Learning NLP Convolutional Neural Networks Neural Network 98

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

Deep Learning Machine Learning Convolutional Neural Networks Computer Vision 52

Marktechpost

APRIL 26, 2024

Graph Machine Learning (Graph ML), especially Graph Neural Networks (GNNs), has emerged to effectively model such data, utilizing deep learning’s message-passing mechanism to capture high-order relationships. Alongside topological structure, nodes often possess textual features providing context.

Large Language Models 122

Large Language Models Machine Learning Neural Network Deep Learning 122

IBM Journey to AI blog

DECEMBER 20, 2023

On retail websites, for instance, machine learning algorithms influence consumer buying decisions by making recommendations based on purchase history. Classification algorithms —predict categorical output variables (e.g., “junk” or “not junk”) by labeling pieces of input data. temperature, salary).

Machine Learning 187

Machine Learning Neural Network Algorithm Computer Vision 187

ML Review

MARCH 31, 2019

We start with an image of a panda, which our neural network correctly recognizes as a “panda” with 57.7% Add a little bit of carefully constructed noise and the same neural network now thinks this is an image of a gibbon with 99.3% This is, clearly, an optical illusion — but for the neural network.

Neural Network 52

Neural Network Machine Learning Deep Learning Explainability 52

Viso.ai

DECEMBER 3, 2023

The recent deep learning algorithms provide robust person detection results. However, deep learning models such as YOLO that are trained for person detection on a frontal view data set still provide good results when applied for overhead view person counting ( TPR of 95%, FPR up to 0.2% ).

Convolutional Neural Networks 98

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 98

Towards AI

APRIL 7, 2024

Regression and classification tasks benefit from this kind of learning. Supervised learning can be applied to GIS applications such as species habitat mapping, land cover categorization, and temperature and precipitation prediction. For example, it takes millions of images and runs them through a training algorithm.

Machine Learning 85

Machine Learning Neural Network Convolutional Neural Networks Deep Learning 85

Marktechpost

DECEMBER 24, 2023

As the demand for sustainable agriculture grows, machine learning emerges as a vital force, reshaping the future of food security and cultivation. These methods address the challenges of traditional approaches, offering more automated, accurate, and robust solutions for identifying and categorizing plant leaf diseases.

Machine Learning 93

Machine Learning Convolutional Neural Networks Neural Network Deep Learning 93

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.

Convolutional Neural Networks 120

Convolutional Neural Networks Neural Network Computer Vision Deep Learning 120

Unite.AI

SEPTEMBER 18, 2023

Blockchain technology can be categorized primarily on the basis of the level of accessibility and control they offer, with Public, Private, and Federated being the three main types of blockchain technologies. Deep learning frameworks can be classified into two categories: Supervised learning, and Unsupervised learning.

Deep Learning 321

Deep Learning Artificial Intelligence Artificial Intelligence AI 321

Pickl AI

APRIL 25, 2023

There are five different subsets of Artificial Intelligence which include Machine Learning, Deep Learning, Robotics, Neural Networks, and NLP. Unsupervised algorithms do not require training a dataset but instead require data to “learn” on its own. What is Deep Learning? What is Robotics?

Artificial Intelligence 52

Artificial Intelligence Artificial Intelligence Neural Network Natural Language Processing 52

Unite.AI

AUGUST 8, 2023

The Technologies Behind Generative Models Generative models owe their existence to deep neural networks, sophisticated structures designed to mimic the human brain's functionality. By capturing and processing multifaceted variations in data, these networks serve as the backbone of numerous generative models.

Generative AI 243

Generative AI ChatGPT Neural Network Convolutional Neural Networks 243

Viso.ai

MAY 24, 2024

Large Action Models (LAMs) are deep learning models that aim to understand instructions and execute complex tasks and actions accordingly. This technique combines learning capabilities and logical reasoning from neural networks and symbolic AI. Extracting information from the patterns learned by neural networks.

Neural Network 52

Neural Network Robotics Automation Large Language Models 52

Viso.ai

FEBRUARY 11, 2024

It is a technique used in computer vision to identify and categorize the main content (objects) in a photo or video. Image classification employs AI-based deep learning models to analyze images and perform object recognition, as well as a human operator. They applied deep CNN over pre-trained ImageNet-1K, with 24.2M

Convolutional Neural Networks 52

Convolutional Neural Networks Computer Vision Neural Network Deep Learning 52

Artificial Corner

JULY 30, 2023

Photo by Shubham Dhage on Unsplash Introduction Large language Models (LLMs) are a subset of Deep Learning. Working of Large Language Models (LLMs) Deep neural networks are used in Large language models to produce results based on patterns discovered from training data. What are large language models used for?

Large Language Models 85

Large Language Models LLM Natural Language Processing Deep Learning 85