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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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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Heartbeat

SEPTEMBER 20, 2023

Pixabay: by Activedia Image captioning combines natural language processing and computer vision to generate image textual descriptions automatically. These algorithms can learn and extract intricate features from input images by using convolutional layers.

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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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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

Be sure to check out his talk, “ Bagging to BERT — A Tour of Applied NLP ,” there! If a Natural Language Processing (NLP) system does not have that context, we’d expect it not to get the joke. We’ll be specifying a “textcat” component, the “model” that will process text into spaCy Doc objects.

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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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Viso.ai

MARCH 20, 2024

Models like GPT 4, BERT, DALL-E 3, CLIP, Sora, etc., Throughout, you’ll gain the following insights: Definition and Scope of Foundation Models How Do Foundation Models Undergo Training And Fine-Tuning Processes? Use Cases for Foundation Models Applications in Pre-trained Language Models like GPT, BERT, Claude, etc.

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Snorkel AI

MARCH 1, 2023

This process results in generalized models capable of a wide variety of tasks, such as image classification, natural language processing, and question-answering, with remarkable accuracy. BERT proved useful in several ways, including quantifying sentiment and predicting the words likely to follow in unfinished sentences.

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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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Viso.ai

APRIL 26, 2024

Thus, these systems are grounded in traditional object detection and natural language processing frameworks. Object detection systems typically use frameworks like Convolutional Neural Networks (CNNs) and Region-based CNNs (R-CNNs). PODs can use the latter to produce an even more interactive experience.

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Pickl AI

AUGUST 14, 2024

Different types of neural networks, such as feedforward, convolutional, and recurrent networks, are designed for specific tasks like image recognition, Natural Language Processing, and sequence modelling. Models such as Long Short-Term Memory (LSTM) networks and Transformers (e.g.,

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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. Transformer Models Transformer models have revolutionised the field of Deep Learning, particularly in Natural Language Processing (NLP).

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ODSC - Open Data Science

MARCH 10, 2025

By 2017, deep learning began to make waves, driven by breakthroughs in neural networks and the release of frameworks like TensorFlow. Sessions on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) started gaining popularity, marking the beginning of data sciences shift toward AI-driven methods.

Data Science 52

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Heartbeat

JUNE 6, 2023

GCNs have been successfully applied to many domains, including computer vision and social network analysis. In recent years, researchers have also explored using GCNs for natural language processing (NLP) tasks, such as text classification , sentiment analysis , and entity recognition. Richong, Z., & Nie, JY.

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Viso.ai

FEBRUARY 25, 2023

Vision Transformer (ViT) have recently emerged as a competitive alternative to Convolutional Neural Networks (CNNs) that are currently state-of-the-art in different image recognition computer vision tasks. Transformer models have become the de-facto status quo in Natural Language Processing (NLP).

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Lexalytics

APRIL 5, 2021

A paper that exemplifies the Classifier Cage Match era is LeCun et al [ 109 ], which pits support vector machines (SVMs), k-nearest neighbor (KNN) classifiers, and convolution neural networks (CNNs) against each other to recognize images from the NORB database. 90,575 trainable parameters, placing it in the small-feature regime.

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Mlearning.ai

MARCH 30, 2023

To understand this let’s take an example of a widely used neural network, CNN (Convolutional Neural Network) CNN is made of two parts: C — Convolution NN — Neural Network The most important part of training is feature extraction which is done by the convolutional part of the CNN.

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Pragnakalp

JANUARY 1, 2024

Since then, in the realms of AI and machine learning, transformer models have emerged as a groundbreaking approach to various language-related tasks. Transformer technology has also heralded generative pretrained transformers (GPTs) and Bidirectional Encoder Representations from Transformers (BERT)."}

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Pickl AI

DECEMBER 9, 2024

Neural Networks Neural networks, particularly deep learning models, introduce a strong inductive bias favouring the discovery of complex, non-linear relationships in large datasets. Let’s explore how it impacts key areas like image classification, natural language processing (NLP), and recommendation systems.

Machine Learning 52

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Viso.ai

MARCH 26, 2024

This enhances the interpretability of AI systems for applications in computer vision and natural language processing (NLP). Using content-based attention it focuses on relevant words to improve accuracy when translating to the target language. The typical architecture of a neural machine translation model (NMT).

Deep Learning 52

Deep Learning Computer Vision Neural Network Natural Language Processing 52

Viso.ai

DECEMBER 22, 2023

Its creators took inspiration from recent developments in natural language processing (NLP) with foundation models. This leap forward is due to the influence of foundation models in NLP, such as GPT and BERT. SAM’s game-changing impact lies in its zero-shot inference capabilities.

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DataRobot Blog

MARCH 9, 2022

This is one of the reasons why detecting sentiment from natural language (NLP or natural language processing ) is a surprisingly complex task. Now we’re dealing with the same words except they’re surrounded by additional information that changes the tone of the overall message from positive to sarcastic.

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TensorFlow

MARCH 29, 2023

ZenDNN is purpose-built to help deep learning application and framework developers improve inference performance on AMD EPYC CPUs across an array of workloads, including computer vision, natural language processing, and recommender systems. TF-ZenDNN We have integrated ZenDNN into high-level AI frameworks for ease of use.

Neural Network 52

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Viso.ai

JULY 9, 2024

Efficient, quick, and cost-effective learning processes are crucial for scaling these models. Transfer Learning is a key technique implemented by researchers and ML scientists to enhance efficiency and reduce costs in Deep learning and Natural Language Processing. Let us understand how this works.

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Viso.ai

DECEMBER 27, 2023

In a computer vision example of contrast learning, we aim to train a tool like a convolutional neural network to bring similar image representations closer and separate the dissimilar ones. It typically uses a convolutional neural network (CNN) architecture, like ResNet , for extracting image features.

Convolutional Neural Networks 52

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Viso.ai

MARCH 26, 2024

Arguably, one of the most pivotal breakthroughs is the application of Convolutional Neural Networks (CNNs) to financial processes. 2: Automated Document Analysis and Processing No.3: Real-world applications range from automating loan approvals to processing insurance claims.

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Pickl AI

NOVEMBER 13, 2024

Unlike Batch Normalization, it doesn’t rely on batch statistics, making it particularly effective in tasks involving RNN or Natural Language Processing. Key Use Case : Layer Normalization can be used in RNNs or transformer-based architectures like GPT and BERT, where sequences are processed one sample at a time.

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Heartbeat

OCTOBER 12, 2023

The Rise of Large Language Models The emergence and proliferation of large language models represent a pivotal chapter in the ongoing AI revolution. These models, powered by massive neural networks, have catalyzed groundbreaking advancements in natural language processing (NLP) and have reshaped the landscape of machine learning.

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Sebastian Ruder

JANUARY 19, 2021

The selection of areas and methods is heavily influenced by my own interests; the selected topics are biased towards representation and transfer learning and towards natural language processing (NLP). Powerful multilingual models that cover around 100 languages emerged including XML-R ( Conneau et al., 2020 ; Rust et al.,

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Heartbeat

FEBRUARY 8, 2024

Cross-modal retrieval is a branch of computer vision and natural language processing that links visual and verbal descriptions. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are often employed to extract meaningful representations from images and text, respectively.

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Marek Rei

NOVEMBER 12, 2019

Below you will find short summaries of a number of different research papers published in the areas of Machine Learning and Natural Language Processing in the past couple of years (2017-2019). link] A bidirectional transformer architecture for pre-training language representations. NAACL 2019. Turakhia, Andrew Y.

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Unite.AI

FEBRUARY 8, 2025

Initially developed to enhance language translation, these models have evolved into a robust framework that excels in sequence modeling, enabling unprecedented efficiency and versatility across various applications. Initially, transformers excelled in language tasks such as translation, summarization, and question-answering.

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Unite.AI

AUGUST 8, 2023

Instead of complex and sequential architectures like Recurrent Neural Networks (RNNs) or Convolutional Neural Networks (CNNs), the Transformer model introduced the concept of attention, which essentially meant focusing on different parts of the input text depending on the context.

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AWS Machine Learning Blog

FEBRUARY 24, 2023

This satisfies the strong MME demand for deep neural network (DNN) models that benefit from accelerated compute with GPUs. These include computer vision (CV), natural language processing (NLP), and generative AI models. The impact is more for models using a convolutional neural network (CNN).

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Marktechpost

APRIL 20, 2024

Deep neural networks like convolutional neural networks (CNNs) have revolutionized various computer vision tasks, from image classification to object detection and segmentation. As models grew larger and more complex, their accuracy soared.

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NLP People

JULY 24, 2019

Especially pre-trained word embeddings such as Word2Vec, FastText and BERT allow NLP developers to jump to the next level. Neural Networks are the workhorse of Deep Learning (cf. Jumping NLP Curves: A Review of Natural Language Processing Research [Review Article]. Toutanova (2018). Goldberg and G.

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