Remove Convolutional Neural Networks Remove Natural Language Processing Remove NLP
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Intent Classification with Convolutional Neural Networks

Analytics Vidhya

It is an integral tool in Natural Language Processing (NLP) used for varied tasks like spam and non-spam email classification, sentiment analysis of movie reviews, detection of hate speech in social […]. The post Intent Classification with Convolutional Neural Networks appeared first on Analytics Vidhya.

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Transfer Learning for NLP: Fine-Tuning BERT for Text Classification

Analytics Vidhya

Introduction With the advancement in deep learning, neural network architectures like recurrent neural networks (RNN and LSTM) and convolutional neural networks (CNN) have shown. The post Transfer Learning for NLP: Fine-Tuning BERT for Text Classification appeared first on Analytics Vidhya.

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Natural Language Processing Using CNNs for Sentence Classification

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Overview Sentence classification is one of the simplest NLP tasks that have a wide range of applications including document classification, spam filtering, and sentiment analysis. A sentence is classified into a class in sentence classification.

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Liquid Neural Networks: Definition, Applications, & Challenges

Unite.AI

Natural Language Understanding Due to their adaptability, real-time learning capabilities, and dynamic topology, Liquid Neural Networks are very good at understanding long Natural Language text sequences. Consider sentiment analysis, an NLP task that aims to understand the underlying emotion behind text.

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Agentic AI: The Foundations Based on Perception Layer, Knowledge Representation and Memory Systems

Marktechpost

Natural Language Processing (NLP): Text data and voice inputs are transformed into tokens using tools like spaCy. These embeddings serve as the language by which subsequent modules (like reasoning or decision-making) interpret the environment.

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What is LSTM – Long Short Term Memory?

Pickl AI

This leads to the vanishing gradient problem, making it difficult for RNNs to retain information from earlier time steps when processing long sequences. LSTMs are crucial for natural language processing tasks. Key Takeaways LSTMs address the vanishing gradient problem in RNNs. In What Applications Are LSTMS Commonly Used?

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data2vec: A Milestone in Self-Supervised Learning

Unite.AI

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.