Marktechpost

MARCH 19, 2024

Traditional methods like 3D convolutional neural networks (CNNs) and video transformers have made significant strides but often struggle to effectively address both local redundancy and global dependencies. Join our Telegram Channel , Discord Channel , and LinkedIn Gr oup. If you like our work, you will love our newsletter.

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Convolutional Neural Networks AI Modeling Neural Network Large Language Models 127

Marktechpost

SEPTEMBER 12, 2023

It leverages the capabilities of deep learning models, such as Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs). This AI Model Uses Image-to-Image Translation to Bring Ancient Fossils to Life appeared first on MarkTechPost. The post Is The Wait for Jurassic Park Over?

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Convolutional Neural Networks AI Modeling Computer Vision Neural Network 136

Unite.AI

SEPTEMBER 21, 2023

AI systems can process large amounts of data to learn patterns and relationships and make accurate and realistic predictions that improve over time. Organizations and practitioners build AI models that are specialized algorithms to perform real-world tasks such as image classification, object detection, and natural language processing.

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AssemblyAI

JANUARY 23, 2025

Unlike many natural language processing (NLP) models, which were historically dominated by recurrent neural networks (RNNs) and, more recently, transformers, wav2letter is designed entirely using convolutional neural networks (CNNs). Ready to implement Speech Recognition in Python? Signup Now

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AWS Machine Learning Blog

MARCH 21, 2025

In this post, we explore how you can use these multi-modal generative AI models to streamline the management of technical documents. Multi-modal generative AI models work well with text extraction from image files, so we start by converting the PDF to a collection of images, one for each page. samples/2003.10304/page_2.png"

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Metadata Convolutional Neural Networks Generative AI Data Scientist 110

Marktechpost

JANUARY 12, 2024

Models tailored for 2D images, such as those based on convolutional neural networks, need to be revised for interpreting complex 3D environments. Models designed for 3D spatial data, like point cloud processors, often fail to effectively leverage the rich detail available in 2D imagery.

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Convolutional Neural Networks Computer Vision AI Modeling Neural Network 128

Unite.AI

NOVEMBER 6, 2023

In recent years, Generative AI has shown promising results in solving complex AI tasks. Modern AI models like ChatGPT , Bard , LLaMA , DALL-E.3 Moreover, Multimodal AI techniques have emerged, capable of processing multiple data modalities, i.e., text, images, audio, and videos simultaneously.

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Unite.AI

SEPTEMBER 9, 2024

Central to this development was a convolutional neural network, trained using Q-learning , which processed raw screen pixels and converted them into game-specific actions based on the current state. The researchers applied this model to seven Atari 2600 games without modifying the architecture or learning algorithm.

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AWS Machine Learning Blog

NOVEMBER 26, 2024

However, LLMs such as Anthropic’s Claude 3 Sonnet on Amazon Bedrock can also perform these tasks using zero-shot prompting, which refers to a prompting technique to give a task to the model without providing specific examples or training for that specific task. The image was generated using the Stability AI model (SDXL 1.0)

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NVIDIA

OCTOBER 4, 2023

The ALERTCalifornia initiative, a collaboration between California’s wildfire fighting agency CAL FIRE and the University of California, San Diego, uses advanced AI developed by DigitalPath. So Ethan Higgins, the company’s system architect, turned to AI.

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Viso.ai

DECEMBER 19, 2023

Today, the use of convolutional neural networks (CNN) is the state-of-the-art method for image classification. The process of creating such labeled data to train AI models needs tedious human work — for instance, to annotate regular traffic situations in autonomous driving. How Does Image Classification Work?

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Marktechpost

JUNE 22, 2023

Like diffusion models, Paella removes varying degrees of noise from tokens representing an image and employs them to generate a new image. The model was trained on 900 million image-text pairs from LAION-5B aesthetic dataset.

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Unite.AI

OCTOBER 22, 2024

From recommending products online to diagnosing medical conditions, AI is everywhere. As AI models become more complex, they demand more computational power, putting a strain on hardware and driving up costs. For example, as model parameters increase, computational demands can increase by a factor of 100 or more.

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Marktechpost

DECEMBER 10, 2024

Artificial Neural Networks (ANNs) have become one of the most transformative technologies in the field of artificial intelligence (AI). Modeled after the human brain, ANNs enable machines to learn from data, recognize patterns, and make decisions with remarkable accuracy. spam detection) and regression (e.g.,

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Viso.ai

OCTOBER 9, 2024

With the rapid development of Convolutional Neural Networks (CNNs) , deep learning became the new method of choice for emotion analysis tasks. Emotion Classification AI Model After pre-processing, the relevant features are retrieved from the pre-processed data containing the detected faces.

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Unite.AI

AUGUST 2, 2023

These limitations are a major issue why an average human mind is able to learn from a single type of data much more effectively when compared to an AI model that relies on separate models & training data to distinguish between an image, text, and speech. They require a high amount of computational power.

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Marktechpost

NOVEMBER 21, 2023

This unique trait significantly reduces computing requirements in comparison to prevailing convolutional neural network architectures, making it ideally suited for field deployment on drones, sensor arrays, and other edge-computing platforms, effectively placing computation closer to its final use.

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Viso.ai

DECEMBER 1, 2023

As Artificial Intelligence (AI) models become more important and widespread in almost every sector, it is increasingly important for businesses to understand how these models work and the potential implications of using them. This guide will provide an overview of AI models and their various applications.

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NVIDIA

OCTOBER 18, 2023

Powerful generative AI models and cloud-native APIs and microservices are coming to the edge. Generative AI is bringing the power of transformer models and large language models to virtually every industry. Generative AI is expected to add $10.5 More than 1.2

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Viso.ai

DECEMBER 10, 2023

Later in this article, we will cover the best-performing deep learning algorithms and AI models for image recognition. The process of creating such labeled data to train AI models requires time-consuming human work, for example, to label images and annotate standard traffic situations in autonomous driving.

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Marktechpost

JUNE 12, 2024

AI can provide precise diagnostics, optimize treatment strategies, and predict disease progression by processing and interpreting large, diverse datasets. Synergies Between AI and Digital Pathology: Advances in DL and image processing are revolutionizing digital pathology.

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AssemblyAI

SEPTEMBER 22, 2023

In August – Meta released a tool for AI-generated audio named AudioCraft and open-sourced all of its underlying models, including MusicGen. Last week – StabilityAI launched StableAudio , a subscription-based platform for creating music with AI models.

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Marktechpost

SEPTEMBER 17, 2024

Similarly, in a vision task with convolutional neural networks, NINO reduced the steps from 8,606 to 4,582, representing a 46.8% This advancement speeds up the training process and opens the door for faster AI model deployment across various domains. reduction in training time. Check out the Paper and GitHub Page.

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Marktechpost

JANUARY 15, 2025

Technical Details and Benefits Deep learning relies on artificial neural networks composed of layers of interconnected nodes. Notable architectures include: Convolutional Neural Networks (CNNs): Designed for image and video data, CNNs detect spatial patterns through convolutional operations.

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Marktechpost

JULY 31, 2024

This article lists the top TensorFlow courses that can help you gain the expertise needed to excel in the field of AI and machine learning. The course provides an overview of TensorFlow Lite and instructions on deploying models on Android, iOS with Swift, and IoT devices.

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Unite.AI

FEBRUARY 21, 2024

This article focuses on a specific type of computer vision model, called Large Vision Models (LVMs), and how they can be leveraged for domain-specific tasks through transfer learning. What are Large Vision Models (LVMs)? LVMs are advanced AI models that process and interpret visual data, typically images or videos.

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Marktechpost

JUNE 27, 2023

Artificial intelligence (AI) has made considerable advances over the past few years, becoming more proficient at activities previously only performed by humans. Yet, hallucination is a problem that has become a big obstacle for AI. What is AI Hallucination? Hallucinations affect people differently than they do AI models.

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Towards AI

SEPTEMBER 1, 2023

The upside of the simplifications is that it allows us to apply more basic and faster AI models. The downside is that, when reducing the dimensionality this way, there’s an intrinsic loss of information, and the models become less generalizable when facing data outside of the design space.

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NYU Center for Data Science

NOVEMBER 23, 2022

A new study “Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs ” by CDS researchers has developed a new approach to the early detection of Alzheimer’s using 3D deep convolutional neural networks.

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Deep Learning Convolutional Neural Networks Neural Network Machine Learning 52

Topbots

OCTOBER 4, 2023

Predictive AI is used to predict future events or outcomes based on historical data. For example, a predictive AI model can be trained on a dataset of customer purchase history data and then used to predict which customers are most likely to churn in the next month. virtual models for advertising campaigns).

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NVIDIA

OCTOBER 26, 2023

The paper describes a model, called DeepSeeColor, that uses a sequence of two convolutional neural networks to reduce backscatter and correct colors in real time on the NVIDIA Jetson Orin NX while undersea. candidate at MIT and AI developer at WARPLab.

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Viso.ai

FEBRUARY 25, 2024

Following that, the development of Convolutional Neural Networks (CNNs) was a watershed moment in the field. The introduction of the Super-Resolution Convolutional Neural Network (SRCNN) later demonstrated that deep learning models could outperform traditional image resolution methods.

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Computer Vision Convolutional Neural Networks Neural Network Deep Learning 98

Marktechpost

DECEMBER 11, 2024

By integrating satellite data, dynamic global vegetation models, and ocean model emulators, the research team developed a near-instantaneous carbon sink model capable of predicting carbon budgets with unprecedented speed and accuracy.

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Convolutional Neural Networks Neural Network AI AI 75

NVIDIA

DECEMBER 21, 2023

Burning for solutions, developers are embracing AI for early detection. DigitalPath, based in Chico, California, has refined a convolutional neural network to spot wildfires. The startup has developed machine learning and AI models using data on fresh produce to help grocers make informed inventory-purchasing decisions.

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Unite.AI

JULY 31, 2023

This approach is known as self-supervised learning , and it’s one of the most efficient methods to build ML and AI models that have the “ common sense ” or background knowledge to solve problems that are beyond the capabilities of AI models today.

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Towards AI

FEBRUARY 21, 2025

Think of AI models like kids in a candy store. Censored or aligned AI? Plus, as someone whos authored books like Deep Reinforcement Learning (Springer Nature) and Convolutional Neural Networks, (Packt), I believe in pushing boundaries, not building walls. No AI jail just yet, thankfully.

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Convolutional Neural Networks Neural Network AI AI 77

Unite.AI

AUGUST 29, 2023

In the past few years, Artificial Intelligence (AI) and Machine Learning (ML) have witnessed a meteoric rise in popularity and applications, not only in the industry but also in academia. This often confines their use to high-capability devices with substantial computing power.

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