Unite.AI

AUGUST 19, 2024

Neural networks have been at the forefront of AI advancements, enabling everything from natural language processing and computer vision to strategic gameplay, healthcare, coding, art and even self-driving cars.

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Marktechpost

AUGUST 3, 2024

However, these neural networks face challenges in interpretation and scalability. The difficulty in understanding learned representations limits their transparency, while expanding the network scale often proves complex. The study also investigates the impact of activation functions on network performance, particularly B-spline.

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Marktechpost

MAY 14, 2024

This article lists the top Deep Learning and Neural Networks books to help individuals gain proficiency in this vital field and contribute to its ongoing advancements and applications. Neural Networks and Deep Learning The book explores both classical and modern deep learning models, focusing on their theory and algorithms.

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AI Weekly

JULY 13, 2023

techxplore.com A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs) Clinical data sharing can facilitate data-driven scientific research, allowing a broader range of questions to be addressed and thereby leading to greater understanding and innovation.

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Unite.AI

MARCH 2, 2025

From early neural networks to todays advanced architectures like GPT-4 , LLaMA , and other Large Language Models (LLMs) , AI is transforming our interaction with technology. These models can process vast amounts of data, generate human-like text, assist in decision-making, and enhance automation across industries.

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Machine Learning Mastery

JUNE 14, 2024

Deep learning models achieve state-of-the-art performance in several computer vision and natural language processing tasks. To help you get started, we’ve compiled a […] The post 5 Free Resources for Understanding Neural Networks appeared first on MachineLearningMastery.com.

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Marktechpost

JULY 12, 2023

Artificial neural networks have advanced significantly over the past few decades, propelled by the notion that more network complexity results in better performance. Modern technology has amazing processing capacity, enabling neural networks to perform these jobs excellently and efficiently.

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Marktechpost

DECEMBER 23, 2023

Deep Neural Networks (DNNs) represent a powerful subset of artificial neural networks (ANNs) designed to model complex patterns and correlations within data. These sophisticated networks consist of multiple layers of interconnected nodes, enabling them to learn intricate hierarchical representations.

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Marktechpost

SEPTEMBER 17, 2024

In deep learning, neural network optimization has long been a crucial area of focus. Training large models like transformers and convolutional networks requires significant computational resources and time. Researchers have been exploring advanced optimization techniques to make this process more efficient.

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Pickl AI

JANUARY 29, 2025

Whether you’re interested in image recognition, natural language processing, or even creating a dating app algorithm, theres a project here for everyone. Natural Language Processing: Powers applications such as language translation, sentiment analysis, and chatbots.

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Marktechpost

MAY 20, 2024

Deep learning is a subset of machine learning that involves training neural networks with multiple layers to recognize patterns and make data-based decisions. TensorFlow Developer Professional Certificate This course teaches how to build and train neural networks using TensorFlow through a hands-on program.

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Marktechpost

MAY 13, 2024

Vision Transformers (ViT) and Convolutional Neural Networks (CNN) have emerged as key players in image processing in the competitive landscape of machine learning technologies. The Rise of Vision Transformers (ViTs) Vision Transformers represent a revolutionary shift in how machines process images.

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Unite.AI

JANUARY 22, 2025

The system's intelligence stems from its neural network-based Concept Processor, which observes and learns from every interaction. Radiologists spend less time manually adjusting display protocols and study descriptions, as the system automatically normalizes imaging data using computer vision and natural language processing (NLP).

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Unite.AI

NOVEMBER 6, 2023

Neural Network: Moving from Machine Learning to Deep Learning & Beyond Neural network (NN) models are far more complicated than traditional Machine Learning models. Advances in neural network techniques have formed the basis for transitioning from machine learning to deep learning.

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Unite.AI

AUGUST 2, 2023

To overcome the challenge presented by single modality models & algorithms, Meta AI released the data2vec, an algorithm that uses the same learning methodology for either computer vision , NLP or speech. For example, there are vocabulary of speech units in speech processing that can define a self-supervised learning task in NLP.

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Unite.AI

MAY 29, 2024

This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction. The encoder processes input data, condensing essential features into a “Context Vector.”

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IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more.

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Viso.ai

DECEMBER 20, 2023

In the field of computer vision, supervised learning and unsupervised learning are two of the most important concepts. In this guide, we will explore the differences and when to use supervised or unsupervised learning for computer vision tasks. We will also discuss which approach is best for specific applications.

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Heartbeat

SEPTEMBER 20, 2023

Pixabay: by Activedia Image captioning combines natural language processing and computer vision to generate image textual descriptions automatically. Image captioning integrates computer vision, which interprets visual information, and NLP, which produces human language.

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AI Weekly

JANUARY 18, 2024

medium.com Robotics From Warehouses to Hospitals: Yujin Robot’s Cutting-Edge Robotic Solutions It transforms traditional factories into smart, interconnected systems, optimizing processes through real-time data, predictive maintenance, and increased customization.

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Marktechpost

JULY 31, 2024

Learning TensorFlow enables you to create sophisticated neural networks for tasks like image recognition, natural language processing, and predictive analytics. Natural Language Processing in TensorFlow This course focuses on building natural language processing systems using TensorFlow.

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Marktechpost

JUNE 3, 2024

Its AI courses offer hands-on training for real-world applications, enabling learners to effectively use Intel’s portfolio in deep learning, computer vision, and more. Deep Learning This course introduces deep learning and covers its techniques, terminology, and fundamental neural network architectures.

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AI Weekly

DECEMBER 14, 2023

theconversation.com Scientists Preparing to Turn on Computer Intended to Simulate Entire Human Brain Researchers at Western Sydney University in Australia have teamed up with tech giants Intel and Dell to build a massive supercomputer intended to simulate neural networks at the scale of the human brain.

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Pickl AI

NOVEMBER 24, 2024

Summary: Autoencoders are powerful neural networks used for deep learning. These powerful neural networks learn to compress data into smaller representations and then reconstruct it back to its original form. An autoencoder is a neural network designed to learn a compressed representation of input data.

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Marktechpost

DECEMBER 16, 2023

Trained on a dataset from six UK hospitals, the system utilizes neural networks, X-Raydar and X-Raydar-NLP, for classifying common chest X-ray findings from images and their free-text reports. The dataset, spanning 13 years, included 2,513,546 chest x-ray studies and 1,940,508 usable free-text radiological reports.

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PyImageSearch

JANUARY 30, 2023

To learn about Computer Vision and Deep Learning for Education, just keep reading. Computer Vision and Deep Learning for Education Benefits Smart Content Artificial Intelligence can help teachers and research experts create innovative and personalized content for their students. Or requires a degree in computer science?

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Pickl AI

JANUARY 26, 2025

These models mimic the human brain’s neural networks, making them highly effective for image recognition, natural language processing, and predictive analytics. Feedforward Neural Networks (FNNs) Feedforward Neural Networks (FNNs) are the simplest and most foundational architecture in Deep Learning.

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Marktechpost

DECEMBER 5, 2023

The Hierarchically Gated Recurrent Neural Network (HGRN) technique developed by researchers from the Shanghai Artificial Intelligence Laboratory and MIT CSAI addresses the challenge of enhancing sequence modeling by incorporating forget gates in linear RNNs. If you like our work, you will love our newsletter.

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Marktechpost

JANUARY 7, 2025

Advancements in neural networks have brought significant changes across domains like natural language processing, computer vision, and scientific computing. Despite these successes, the computational cost of training such models remains a key challenge.

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Unite.AI

OCTOBER 22, 2024

Put simply, if we double the input size, the computational needs can increase fourfold. AI models like neural networks , used in applications like Natural Language Processing (NLP) and computer vision , are notorious for their high computational demands.

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Neural Network Convolutional Neural Networks Deep Learning Natural Language Processing 147

Marktechpost

FEBRUARY 22, 2024

In computer vision, convolutional networks acquire a semantic understanding of images through extensive labeling provided by experts, such as delineating object boundaries in datasets like COCO or categorizing images in ImageNet.

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Flipboard

MARCH 27, 2023

Project Structure Accelerating Convolutional Neural Networks Parsing Command Line Arguments and Running a Model Evaluating Convolutional Neural Networks Accelerating Vision Transformers Evaluating Vision Transformers Accelerating BERT Evaluating BERT Miscellaneous Summary Citation Information What’s New in PyTorch 2.0?

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Marktechpost

APRIL 12, 2024

Deep learning architectures have revolutionized the field of artificial intelligence, offering innovative solutions for complex problems across various domains, including computer vision, natural language processing, speech recognition, and generative models.

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IBM Journey to AI blog

OCTOBER 12, 2023

However, AI capabilities have been evolving steadily since the breakthrough development of artificial neural networks in 2012, which allow machines to engage in reinforcement learning and simulate how the human brain processes information. Human intervention was required to expand Siri’s knowledge base and functionality.

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Marktechpost

MARCH 30, 2024

The most significant feature of PyTorch is its dynamic computational graph, which enables smooth changes and an instinctive coding style. PyTorch boasts a robust ecosystem with tools and libraries for computer vision, natural language processing, and more.

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Marktechpost

DECEMBER 12, 2024

A Deep Neural Network (DNN) is an artificial neural network that features multiple layers of interconnected nodes, also known as neurons. Each neuron processes input data by applying weights, biases, and an activation function to generate an output. These layers include an input, multiple hidden, and output layers.

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IBM Journey to AI blog

FEBRUARY 14, 2024

Voice-based queries use natural language processing (NLP) and sentiment analysis for speech recognition so their conversations can begin immediately. Running on neural networks , computer vision enables systems to extract meaningful information from digital images, videos and other visual inputs.

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