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.

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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). What sets wav2letter apart is its unique architecture.

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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.

Deep Learning 136

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Unite.AI

AUGUST 2, 2023

To tackle the issue of single modality, Meta AI released the data2vec, the first of a kind, self supervised high-performance algorithm to learn patterns information from three different modalities: image, text, and speech. Why Does the AI Industry Need the Data2Vec Algorithm?

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Unite.AI

NOVEMBER 6, 2023

The simplest NN – Multi-layer perceptron (MLP) consists of several neurons connected together to understand information and perform tasks, similar to how a human brain functions. Advances in neural network techniques have formed the basis for transitioning from machine learning to deep learning.

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ODSC - Open Data Science

NOVEMBER 28, 2024

NVIDIA’s AI Tools Suite to Aid in Accelerated Humanoid Robotics Development NVIDIA’s AI tools suite may drive developers toward complex machine learning and natural language processing solutions. In all likelihood, AI technology and humanoid robotics will progress hand in hand in the coming years.

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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. The convolution layer applies filters (kernels) over input data, extracting essential features such as edges, textures, or shapes.

Deep Learning 52

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Marktechpost

MARCH 26, 2024

The transformer architecture has improved natural language processing, with recent advancements achieved through scaling efforts from millions to billion-parameter models. Observations indicate diminishing returns with increased model depth, mirroring challenges in deep convolutional neural networks for computer vision.

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Towards AI

MARCH 3, 2025

Can machines understand human language? These questions are addressed by the field of Natural Language processing, which allows machines to mimic human comprehension and usage of natural language. Last Updated on March 3, 2025 by Editorial Team Author(s): SHARON ZACHARIA Originally published on Towards AI.

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Marktechpost

NOVEMBER 10, 2023

Traditionally, Convolutional Neural Networks (CNNs) have been the go-to models for processing image data, leveraging their ability to extract meaningful features and classify visual information.

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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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Unite.AI

MAY 24, 2024

In modern machine learning and artificial intelligence frameworks, transformers are one of the most widely used components across various domains including GPT series, and BERT in Natural Language Processing, and Vision Transformers in computer vision tasks.

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Unite.AI

APRIL 26, 2024

The advancements in large language models have significantly accelerated the development of natural language processing , or NLP. These extend far beyond the traditional text-based processing of LLMs to include multimodal interactions.

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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

DECEMBER 10, 2024

Artificial Neural Networks are computational systems inspired by the human brain’s structure and functionality. They consist of interconnected layers of nodes (neurons) that process information by assigning weights and applying activation functions. How Do Artificial Neural Networks Work?

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Unite.AI

OCTOBER 22, 2024

AI models like neural networks , used in applications like Natural Language Processing (NLP) and computer vision , are notorious for their high computational demands. Computer vision tasks rely heavily on matrix operations and have also used sub-quadratic techniques to streamline convolutional processes.

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Marktechpost

JANUARY 15, 2025

These limitations are particularly significant in fields like medical imaging, autonomous driving, and natural language processing, where understanding complex patterns is essential. Recurrent Neural Networks (RNNs): Well-suited for sequential data like time series and text, RNNs retain context through loops.

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The MLOps Blog

MARCH 6, 2025

State space models for natural language processing State Space Models (SSMs), long established in time series analysis, have been utilized as trainable sequence models for decades. Around 2020, their ability to efficiently handle long sequences spurred significant progress in adapting them for natural language processing (NLP).

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Artificial Corner

JANUARY 23, 2025

Subscribe now #3 Natural Language Processing Course in Python This is a short yet useful 2-hour NLP course for anyone interested in the field of Natural Language Processing. NLP is a branch of artificial intelligence that allows machines to understand human language.

Data Science 95

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IBM Journey to AI blog

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data.

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Towards AI

JANUARY 14, 2025

Transformers have revolutionized natural language processing (NLP), powering models like GPT and BERT. The goal was to see if I could accurately identify these digits using a Transformer-based approach, which feels quite different from the traditional Convolutional Neural Network (CNN) methods I was more familiar with.

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IBM Journey to AI blog

SEPTEMBER 24, 2024

” When Guerena’s team first started working with smartphone images, they used convolutional neural networks (CNNs). ” Guerena’s team is now working on integrating speech-to-text and natural language processing alongside computer vision in the systems they’re building.

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John Snow Labs

MAY 23, 2023

Visual question answering (VQA), an area that intersects the fields of Deep Learning, Natural Language Processing (NLP) and Computer Vision (CV) is garnering a lot of interest in research circles. A VQA system takes free-form, text-based questions about an input image and presents answers in a natural language format.

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Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

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ODSC - Open Data Science

APRIL 28, 2023

If a Natural Language Processing (NLP) system does not have that context, we’d expect it not to get the joke. Each has a single representation for the word “well”, which combines the information for “doing well” with “wishing well”. You naturally link those two words, as you do with “the” and “movie”.

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Unite.AI

JULY 31, 2023

Despite their capabilities, AI & ML models are not perfect, and scientists are working towards building models that are capable of learning from the information they are given, and not necessarily relying on labeled or annotated data.

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Marktechpost

JUNE 3, 2024

Numerous groundbreaking models—including ChatGPT, Bard, LLaMa, AlphaFold2, and Dall-E 2—have surfaced in different domains since the Transformer’s inception in Natural Language Processing (NLP). The positional embeddings used by the encoder and decoder are completely different.

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Becoming Human

MARCH 16, 2023

This post includes the fundamentals of graphs, combining graphs and deep learning, and an overview of Graph Neural Networks and their applications. Through the next series of this post here , I will try to make an implementation of Graph Convolutional Neural Network. So, let’s get started! What are Graphs?

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Topbots

OCTOBER 4, 2023

Here are a few examples across various domains: Natural Language Processing (NLP) : Predictive NLP models can categorize text into predefined classes (e.g., Here are a few examples across various domains: Natural Language Processing (NLP) : Predictive NLP models can categorize text into predefined classes (e.g.,

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Google Research AI blog

MAY 26, 2023

Posted by Julian Eisenschlos, Research Software Engineer, Google Research Visual language is the form of communication that relies on pictorial symbols outside of text to convey information. Answering the question requires reading the information and computing the sum and the difference. Example from ChartQA.

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Viso.ai

DECEMBER 10, 2023

Training of Neural Networks for Image Recognition The images from the created dataset are fed into a neural network algorithm. The training of an image recognition algorithm makes it possible for convolutional neural network image recognition to identify specific classes.

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Marktechpost

JUNE 27, 2023

It takes in user information, processes it, and comes up with a solution. The system uses OpenAI technology to comprehend and interpret spoken and written language. It can respond to the user automatically or prompt them to enter further information. tools, allowing for insightful analysis and lightning-fast processing.

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Lexalytics

APRIL 5, 2021

.” And Simard, Steinkraus and Platt [ 27 ] say: “After being extremely popular in the early 1990s, neural networks have fallen out of favor in research in the last 5 years. The CNN was a 6-layer neural net with 132 convolution kernels and (don’t laugh!) And in 2012, Alex Krizhevsky, Ilya Sutskever and Geoffrey E.

Neural Network 52

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Marktechpost

DECEMBER 12, 2024

The deep aspect of DNNs comes from multiple hidden layers, which allow the network to learn and model complex patterns and relationships in data. DNNs are the backbone of many advanced artificial intelligence applications, including image recognition, natural language processing, and autonomous systems.

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Marktechpost

NOVEMBER 27, 2024

At its core, machine learning algorithms seek to identify patterns within data, enabling computers to learn and adapt to new information. 8) Principal Component Analysis (PCA) Principal Component Analysis (PCA) is a statistical method used to reduce the dimensionality of a dataset while preserving most of the information.

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Mlearning.ai

MAY 30, 2023

As we know that Convolutional Neural Network (CNN) is used for structured arrays of data such as image data. RNNs contain an internal memory that enables them to capture and use context from prior inputs, in contrast to standard feedforward neural networks, which process data strictly sequentially.

Neural Network 52

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Viso.ai

OCTOBER 10, 2024

The identification of regularities in data can then be used to make predictions, categorize information, and improve decision-making processes. The goal of pattern recognition is based on the idea that the decision-making process of a human being is somewhat related to the recognition of patterns.

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