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. identifying the “emotional tone” of a particular document). In this post, I’ll be demonstrating two deep learning approaches to sentiment analysis. deep” architecture). It’s all about context!

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Mlearning.ai

JANUARY 26, 2023

Introduction Natural language processing (NLP) sentiment analysis is a powerful tool for understanding people’s opinions and feelings toward specific topics. NLP sentiment analysis uses natural language processing (NLP) to identify, extract, and analyze sentiment from text data.

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Heartbeat

FEBRUARY 8, 2023

Source: Author The field of natural language processing (NLP), which studies how computer science and human communication interact, is rapidly growing. By enabling robots to comprehend, interpret, and produce natural language, NLP opens up a world of research and application possibilities.

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Data Science 101

NOVEMBER 29, 2019

Amazon Comprehend launches real-time classification Amazon Comprehend is a service which uses Natural Language Processing (NLP) to examine documents. Comprehend can now be used to classify documents in real-time. Document classification no longer needs to be performed in batch processes.

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DrivenData Labs

MAY 18, 2023

I work on machine learning for natural language processing, and I’m particularly interested in few-shot learning, lifelong learning, and societal and health applications such as abuse detection, misinformation, mental ill-health detection, and language assessment.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

In today’s rapidly evolving landscape of artificial intelligence, deep learning models have found themselves at the forefront of innovation, with applications spanning computer vision (CV), natural language processing (NLP), and recommendation systems. use train_dataloader in the rest of the training logic.

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Towards AI

DECEMBER 28, 2023

The advent of more powerful personal computers paved the way for the gradual acceptance of deep learning-based methods. The introduction of attention mechanisms has notably altered our approach to working with deep learning algorithms, leading to a revolution in the realms of computer vision and natural language processing (NLP).

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Dataconomy

FEBRUARY 26, 2024

Their architecture is a beacon of parallel processing capability, enabling the execution of thousands of tasks simultaneously. This attribute is particularly beneficial for algorithms that thrive on parallelization, effectively accelerating tasks that range from complex simulations to deep learning model training.

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Heartbeat

FEBRUARY 9, 2023

Photo by Brooks Leibee on Unsplash Introduction Natural language processing (NLP) is the field that gives computers the ability to recognize human languages, and it connects humans with computers. SpaCy is a free, open-source library written in Python for advanced Natural Language Processing.

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DagsHub

JULY 25, 2024

Source: Author Introduction Deep learning, a branch of machine learning inspired by biological neural networks, has become a key technique in artificial intelligence (AI) applications. Deep learning methods use multi-layer artificial neural networks to extract intricate patterns from large data sets.

Deep Learning 52

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Heartbeat

JULY 18, 2023

Source: Author Natural Language Processing (NLP) is a field of study focused on allowing computers to understand and process human language. There are many different NLP techniques and tools available, including the R programming language. You must have defined your /.comet.yml

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Pickl AI

JANUARY 8, 2025

Summary: Attention mechanism in Deep Learning enhance AI models by focusing on relevant data, improving efficiency and accuracy. Introduction Deep Learning has revolutionised artificial intelligence, driving advancements in natural language processing, computer vision, and more. from 2024 to 2032.

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Data Science Dojo

SEPTEMBER 11, 2023

From virtual assistants like Siri and Alexa to personalized recommendations on streaming platforms, chatbots, and language translation services, language models surely are the engines that power it all. First Generation: Early language models used simple statistical techniques like n-grams to predict words based on the previous ones.

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Pickl AI

SEPTEMBER 25, 2024

Summary: This blog delves into 20 Deep Learning applications that are revolutionising various industries in 2024. From healthcare to finance, retail to autonomous vehicles, Deep Learning is driving efficiency, personalization, and innovation across sectors.

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AWS Machine Learning Blog

JUNE 18, 2024

Manually identifying all mentions of specific types of information in documents is extremely time-consuming and labor-intensive. This process must be repeated for every new document and entity type, making it impractical for processing large volumes of documents at scale.

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AWS Machine Learning Blog

DECEMBER 12, 2024

For the detailed list of pre-set values, refer to the SDK documentation. For a full list of the available configs, including compute and networking, refer to the SDK documentation. We encourage you to try out the ModelTrainer class by referring to the SDK documentation and sample notebooks on the GitHub repo.

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Data Science Blog

FEBRUARY 23, 2023

1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves.

Supervised Learning 264

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Mlearning.ai

APRIL 1, 2023

NLP A Comprehensive Guide to Word2Vec, Doc2Vec, and Top2Vec for Natural Language Processing In recent years, the field of natural language processing (NLP) has seen tremendous growth, and one of the most significant developments has been the advent of word embedding techniques. DM Architecture.

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Flipboard

JUNE 20, 2023

We present the results of recent performance and power draw experiments conducted by AWS that quantify the energy efficiency benefits you can expect when migrating your deep learning workloads from other inference- and training-optimized accelerated Amazon Elastic Compute Cloud (Amazon EC2) instances to AWS Inferentia and AWS Trainium.

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AWS Machine Learning Blog

NOVEMBER 15, 2024

This significant improvement showcases how the fine-tuning process can equip these powerful multimodal AI systems with specialized skills for excelling at understanding and answering natural language questions about complex, document-based visual information. For a detailed walkthrough on fine-tuning the Meta Llama 3.2

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Towards AI

JULY 20, 2023

Natural Language Processing Getting desirable data out of published reports and clinical trials and into systematic literature reviews (SLRs) — a process known as data extraction — is just one of a series of incredibly time-consuming, repetitive, and potentially error-prone steps involved in creating SLRs and meta-analyses.

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Dataconomy

JUNE 5, 2023

As higher-quality images need more processing power, it is unclear if Midjourney is near to achieving this objective; yet, this is certainly one of the most anticipated additions of Midjourney V6. Smarter natural language processing Natural language processing is another area in which Midjourney v6 will shine.

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PyImageSearch

SEPTEMBER 23, 2024

Home Table of Contents Deploying a Vision Transformer Deep Learning Model with FastAPI in Python What Is FastAPI? You’ll learn how to structure your project for efficient model serving, implement robust testing strategies with PyTest, and manage dependencies to ensure a smooth deployment process. Testing main.py

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AWS Machine Learning Blog

MAY 24, 2023

Intelligent document processing (IDP) is a technology that automates the processing of high volumes of unstructured data, including text, images, and videos. Natural language processing (NLP) is one of the recent developments in IDP that has improved accuracy and user experience.

AI 84

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Towards AI

JULY 20, 2023

Jerome in his Study | Durer NATURAL LANGUAGE PROCESSING (NLP) WEEKLY NEWSLETTER The NLP Cypher | 03.14.21 Let’s talk about “Cryptonite: How I Stopped Worrying and Learned(?) LineFlow was designed to use in all deep learning… github.com Repo Cypher ?‍? A new update is out!

Deep Learning 98

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AWS Machine Learning Blog

JANUARY 31, 2024

Embeddings play a key role in natural language processing (NLP) and machine learning (ML). Text embedding refers to the process of transforming text into numerical representations that reside in a high-dimensional vector space. She helps customers to build, train and deploy large machine learning models at scale.

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AWS Machine Learning Blog

NOVEMBER 10, 2023

In the recent past, using machine learning (ML) to make predictions, especially for data in the form of text and images, required extensive ML knowledge for creating and tuning of deep learning models. These capabilities include pre-trained models for image, text, and document data types.

Machine Learning 110

Machine Learning Machine Learning ML ML 110

Data Science Dojo

AUGUST 22, 2023

Unlocking the Power of LLM Use-Cases: AI applications now excel at summarizing articles, weaving narratives, and sparking conversations, all thanks to advanced large language models. Law Technology has greatly transformed the legal field, streamlining tasks like research and document drafting that once consumed lawyers’ time.

Natural Language Processing 195

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ODSC - Open Data Science

APRIL 3, 2023

Question Answering is the task in Natural Language Processing that involves answering questions posed in natural language. The goal of QA is to create models that can understand the nuances of a question and some given evidence documents to provide an accurate and concise answer. Haritz Puerto is a Ph.D.

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AWS Machine Learning Blog

JUNE 26, 2024

This is because trades involve different counterparties and there is a high degree of variation among documents containing commercial terms (such as trade date, value date, and counterparties). Artificial intelligence and machine learning (AI/ML) technologies can assist capital market organizations overcome these challenges.

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AWS Machine Learning Blog

SEPTEMBER 16, 2024

RAG is an approach that combines information retrieval techniques with natural language processing (NLP) to enhance the performance of text generation or language modeling tasks. This method involves retrieving relevant information from a large corpus of text data and using it to augment the generation process.

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AssemblyAI

SEPTEMBER 4, 2024

  Today, deep learning technology, heavily influenced by Baidu’s seminal paper Deep Speech: Scaling up end-to-end speech recognition , dominates the field. In the next section, we’ll discuss how these deep learning approaches work in more detail. How does speech recognition work?

Deep Learning 122

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Towards AI

JULY 23, 2023

In the first part of the series, we talked about how Transformer ended the sequence-to-sequence modeling era of Natural Language Processing and understanding. Having used multiple source documents, there have been duplicates and resulted in a huge set, which is impossible to train a model on, due to lack of processing power.

Natural Language Processing 98

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Pickl AI

DECEMBER 26, 2024

Computer Hardware At the core of any Generative AI system lies the computer hardware, which provides the necessary computational power to process large datasets and execute complex algorithms. Foundation Models Foundation models are pre-trained deep learning models that serve as the backbone for various generative applications.

AI 52

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Dataconomy

FEBRUARY 21, 2024

Choose Your Framework & Environment Flexibility is key : Google Gemma AI works seamlessly with popular deep learning frameworks like JAX, PyTorch, and Keras 3.0 Explore and Experiment Google AI Gemma Website : Find quickstart guides, code samples, and detailed documentation. TensorFlow backend).

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AssemblyAI

MARCH 21, 2023

Footnotes These consisted of two evolving document graphs based on citation data and Reddit post data (predicting paper and post categories, respectively), and a multigraph generalization experiment based on a dataset of protein-protein interactions (predicting protein functions).

Deep Learning 126

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AWS Machine Learning Blog

JUNE 15, 2023

Dive into Deep Learning ( D2L.ai ) is an open-source textbook that makes deep learning accessible to everyone. First, we put the source documents, reference documents, and parallel data training set in an S3 bucket. The ParallelData folder holds the parallel data input file prepared in the previous step.

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