Marktechpost

MAY 3, 2024

Natural Language Processing (NLP) is integral to artificial intelligence, enabling seamless communication between humans and computers. This interdisciplinary field incorporates linguistics, computer science, and mathematics, facilitating automatic translation, text categorization, and sentiment analysis.

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Unite.AI

MAY 29, 2024

With nine times the speed of the Nvidia A100, these GPUs excel in handling deep learning workloads. This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction.

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IBM Journey to AI blog

FEBRUARY 23, 2024

Beyond the simplistic chat bubble of conversational AI lies a complex blend of technologies, with natural language processing (NLP) taking center stage. NLP translates the user’s words into machine actions, enabling machines to understand and respond to customer inquiries accurately. What makes a good AI conversationalist?

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Unite.AI

AUGUST 28, 2023

With advancements in deep learning, natural language processing (NLP), and AI, we are in a time period where AI agents could form a significant portion of the global workforce. Neural Networks & Deep Learning : Neural networks marked a turning point, mimicking human brain functions and evolving through experience.

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IBM Journey to AI blog

DECEMBER 20, 2023

And retailers frequently leverage data from chatbots and virtual assistants, in concert with ML and natural language processing (NLP) technology, to automate users’ shopping experiences. A semi-supervised learning model might use unsupervised learning to identify data clusters and then use supervised learning to label the clusters.

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John Snow Labs

JUNE 2, 2023

Sentiment analysis, also known as opinion mining, is the process of computationally identifying and categorizing the subjective information contained in natural language text. Deep learning models can automatically learn features and representations from raw text data, making them well-suited for sentiment analysis tasks.

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The MLOps Blog

MARCH 23, 2023

NLP models in commercial applications such as text generation systems have experienced great interest among the user. These models have achieved various groundbreaking results in many NLP tasks like question-answering, summarization, language translation, classification, paraphrasing, et cetera.

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Marktechpost

APRIL 26, 2024

Graph Machine Learning (Graph ML), especially Graph Neural Networks (GNNs), has emerged to effectively model such data, utilizing deep learning’s message-passing mechanism to capture high-order relationships. Foundation Models (FMs) have revolutionized NLP and vision domains in the broader AI spectrum.

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Unite.AI

JUNE 11, 2024

Third, the NLP Preset is capable of combining tabular data with NLP or Natural Language Processing tools including pre-trained deep learning models and specific feature extractors. Finally, the CV Preset works with image data with the help of some basic tools.

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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. What is text mining?

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Topbots

APRIL 11, 2023

BERT by Google Summary In 2018, the Google AI team introduced a new cutting-edge model for Natural Language Processing (NLP) – BERT , or B idirectional E ncoder R epresentations from T ransformers. This model marked a new era in NLP with pre-training of language models becoming a new standard. Where to learn more about this research?

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IBM Journey to AI blog

AUGUST 1, 2023

It’s the underlying engine that gives generative models the enhanced reasoning and deep learning capabilities that traditional machine learning models lack. They can also perform self-supervised learning to generalize and apply their knowledge to new tasks. That’s where the foundation model enters the picture.

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Marktechpost

OCTOBER 28, 2023

A model’s capacity to generalize or effectively apply its learned knowledge to new contexts is essential to the ongoing success of Natural Language Processing (NLP). To address that, a group of researchers from Meta has proposed a thorough taxonomy to describe and comprehend NLP generalization research.

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Heartbeat

AUGUST 29, 2023

Deep learning is a branch of machine learning that makes use of neural networks with numerous layers to discover intricate data patterns. Deep learning models use artificial neural networks to learn from data. It is a tremendous tool with the ability to completely alter numerous sectors.

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Explosion

NOVEMBER 9, 2016

now features deep learning models for named entity recognition, dependency parsing, text classification and similarity prediction based on the architectures described in this post. You can now also create training and evaluation data for these models with Prodigy , our new active learning-powered annotation tool.

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Artificial Corner

JULY 30, 2023

Photo by Shubham Dhage on Unsplash Introduction Large language Models (LLMs) are a subset of Deep Learning. Some Terminologies related to Artificial Intelligence (Ai) Deep Learning is a technique used in artificial intelligence (AI) that teaches computers to interpret data in a manner modeled after the human brain.

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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). The introduction of the Transformer model was a significant leap forward for the concept of attention in deep learning. Vaswani et al. It does this by applying self-attention to sequences of image patches.

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Heartbeat

JUNE 22, 2023

Natural language processing (NLP) can help with this. In this post, we’ll look at how natural language processing (NLP) may be utilized to create smart chatbots that can comprehend and reply to natural language requests. What is NLP? Sentiment analysis, language translation, and speech recognition are a few NLP applications.

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AssemblyAI

JANUARY 23, 2024

Large Language Models, or LLMs , are Machine Learning models that understand, generate, and interact with human language.  Its AI Conversational Intelligence feature also helps its customers more efficiently process call data at scale by auto-scoring and categorizing key sections of customer calls. 

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ODSC - Open Data Science

SEPTEMBER 12, 2024

Now that artificial intelligence has become more widely accepted, some daring companies are looking at natural language processing (NLP) technology as the solution. Naturally, its high penetration rate has given way to exploration into machine learning subsets like deep learning and NLP.

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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. While explorative pattern recognition aims to identify data patterns in general, descriptive pattern recognition starts by categorizing the detected patterns. – Learn more.

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Marktechpost

OCTOBER 18, 2023

The recent results of machine learning in drug discovery have been largely attributed to graph and geometric deep learning models. Like other deep learning techniques, they need a lot of training data to provide excellent modeling accuracy.

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AssemblyAI

MAY 14, 2024

It automatically categorizes, summarizes, and extracts actionable insights from customer calls, such as flagging questions and complaints. It uses state-of-the-art deep learning models trained on diverse, large datasets to catch subtle patterns in speech that may indicate depression or anxiety. For example, Corti.ai

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Towards AI

FEBRUARY 13, 2023

A practical guide on how to perform NLP tasks with Hugging Face Pipelines Image by Canva With the libraries developed recently, it has become easier to perform deep learning analysis. Hugging Face is a platform that provides pre-trained language models for NLP tasks such as text classification, sentiment analysis, and more.

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Heartbeat

AUGUST 23, 2023

Enter Natural Language Processing (NLP) and its transformational power. This is the promise of NLP: to transform the way we approach legal discovery. The seemingly impossible chore of sorting through mountains of legal documents can be accomplished with astonishing efficiency and precision using NLP.

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John Snow Labs

MAY 19, 2023

Natural Language Processing ( NLP ) is changing the way the legal sector operates. According to a report, the NLP market size is expected to reach $27.6 NLP understands and predicts law, converts unstructured text into a meaningful format that computers can understand and analyze. billion by 2026. are entity categories.

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IBM Journey to AI blog

APRIL 18, 2024

Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles. NLP techniques help them parse the nuances of human language, including grammar, syntax and context. Most experts categorize it as a powerful, but narrow AI model.

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ODSC - Open Data Science

MARCH 13, 2023

They are experts in machine learning, NLP, deep learning, data engineering, MLOps, and data visualization. Learn more about a few of our ODSC East 2023 instructors, their backgrounds in education, and why they’re fit for imparting their knowledge. Dr. Jon Krohn Chief Data Scientist | Nebula.io

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ODSC - Open Data Science

MARCH 20, 2023

Confirmed sessions include: Self-Supervised and Unsupervised Learning for Conversational AI and NLP NLP Fundamentals Applying Responsible AI with Open-Source Tools And more to come soon!

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ODSC - Open Data Science

FEBRUARY 15, 2023

With courses ranging in skill level from beginner to advanced and covering topics such as machine learning, NLP, MLOps, data engineering, deep learning, and much, much more, you’ll have the opportunity to craft a conference experience that matches your particular needs and interests.

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IBM Journey to AI blog

OCTOBER 16, 2023

Voice-based queries use Natural Language Processing (NLP) and sentiment analysis for speech recognition. This communication can involve speech recognition, speech-to-text conversion, NLP, or text-to-speech. Reinforcement learning uses ML to train models to identify and respond to cyberattacks and detect intrusions.

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Marktechpost

JANUARY 4, 2024

The system’s error detection mechanism is designed to identify and categorize failures during execution promptly. As LLMs grow in complexity and size, systems like Unicron will play an increasingly vital role in harnessing their full potential, driving the frontiers of AI and NLP research forward. Check out the Paper.

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ODSC - Open Data Science

MAY 24, 2023

Generative NLP Models in Customer Service: Evaluating Them, Challenges, and Lessons Learned in Banking Editor’s note: The authors are speakers for ODSC Europe this June. Be sure to check out their talk, “ Generative NLP models in customer service. Challenges and lessons learned in a real use case in banking ,” there!

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IBM Journey to AI blog

MAY 17, 2024

A full one-third of consumers found their early customer support and chatbot experiences that use natural language processing (NLP) so disappointing that they didn’t want to engage with the technology again. And  And the centrality of these experiences isn’t limited to B2C vendors.

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Marktechpost

OCTOBER 22, 2023

Words and phrases can be effectively represented as vectors in a high-dimensional space using embeddings, making them a crucial tool in the field of natural language processing (NLP). FastEmbed is a compact yet powerful library for generating embeddings in large databases. FastEmbed is as accurate as other embedding methods, if not more so.

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AWS Machine Learning Blog

NOVEMBER 1, 2024

This tagging structure categorizes costs and allows assessment of usage against budgets. ListTagsForResource : Fetches the tags associated with a specific Bedrock resource, helping users understand how their resources are categorized. He focuses on Deep learning including NLP and Computer Vision domains.

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Marktechpost

JULY 4, 2023

A model’s parameters are the components learned from previous training data and, in essence, establish the model’s proficiency on a task, such as text generation. Natural language processing (NLP) activities, including speech-to-text, sentiment analysis, text summarization, spell-checking, token categorization, etc.,

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