Dataconomy

SEPTEMBER 4, 2023

Text analytics: Text analytics, also known as text mining, deals with unstructured text data, such as customer reviews, social media comments, or documents. It uses natural language processing (NLP) techniques to extract valuable insights from textual data. Poor data integration can lead to inaccurate insights.

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Analytics Analytics Big Data Analytics Big Data Analytics 203

ODSC - Open Data Science

MARCH 10, 2025

The Early Years: Laying the Foundations (20152017) In the early years, data science conferences predominantly focused on foundational topics like data analytics , visualization , and the rise of big data. The Deep Learning Boom (20182019) Between 2018 and 2019, deep learning dominated the conference landscape.

Data Science 52

Data Science Deep Learning Deep Learning Machine Learning 52

Alation

FEBRUARY 20, 2020

While this requires technology – AI, machine learning, log parsing, natural language processing,metadata management, this technology must be surfaced in a form accessible to business users – the data catalog. The Forrester Wave : Machine Learning Data Catalogs, Q2 2018.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing Analytics 52

AWS Machine Learning Blog

AUGUST 16, 2023

The images document the land cover, or physical surface features, of ten European countries between June 2017 and May 2018. Jeremy Anderson is a Director & Data Scientist at Travelers on the AI & Automation Accelerator team. His specialty is Natural Language Processing (NLP) and is passionate about deep learning.

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IBM Journey to AI blog

AUGUST 1, 2023

Foundation models can be trained to perform tasks such as data classification, the identification of objects within images (computer vision) and natural language processing (NLP) (understanding and generating text) with a high degree of accuracy. An open-source model, Google created BERT in 2018.

AI 70

AI AI Machine Learning Machine Learning 70

Snorkel AI

MARCH 1, 2023

Foundation models are large AI models trained on enormous quantities of unlabeled data—usually through self-supervised learning. 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.

Natural Language Processing 83

Natural Language Processing Supervised Learning Machine Learning Machine Learning 83

Heartbeat

AUGUST 23, 2023

But what if there was a technique to quickly and accurately solve this language puzzle? Enter Natural Language Processing (NLP) and its transformational power. But what if there was a way to unravel this language puzzle swiftly and accurately?

Natural Language Processing 52

Natural Language Processing Algorithm Artificial Intelligence Artificial Intelligence 52

AWS Machine Learning Blog

APRIL 19, 2023

Since 2018, our team has been developing a variety of ML models to enable betting products for NFL and NCAA football. Our data scientists train the model in Python using tools like PyTorch and save the model as PyTorch scripts. Business requirements We are the US squad of the Sportradar AI department.

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ML ML Deep Learning Deep Learning 98

AWS Machine Learning Blog

FEBRUARY 10, 2023

Quantitative evaluation We utilize 2018–2020 season data for model training and validation, and 2021 season data for model evaluation. Haibo Ding is a senior applied scientist at Amazon Machine Learning Solutions Lab. He is broadly interested in Deep Learning and Natural Language Processing.

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ML ML Machine Learning Machine Learning 98

AWS Machine Learning Blog

AUGUST 7, 2023

AWS received about 100 samples of labeled data from the customer, which is a lot less than the 1,000 samples recommended for fine-tuning an LLM in the data science community. Improving Language Understanding by Generative Pre-Training” Devlin et al., Safa Tinaztepe is a full-stack data scientist with AWS Professional Services.

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Heartbeat

JANUARY 29, 2024

Imagine an AI system that becomes proficient in many tasks through extensive training on each specific problem and a higher-order learning process that distills valuable insights from previous learning endeavors. Natural Language Processing: With Meta-Learning, language models can be generalized across various languages and dialects.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing Algorithm 52

AWS Machine Learning Blog

OCTOBER 2, 2024

About the Authors Maira Ladeira Tanke is a Senior Generative AI Data Scientist at AWS. Her work spans speech recognition, natural language processing, and large language models. Andrew Gordon Wilson before joining Amazon in 2018.

AI 100

AI AI AWS Machine Learning 100

Heartbeat

MARCH 15, 2023

But now, a computer can be taught to comprehend and process human language through Natural Language Processing (NLP), which was implemented, to make computers capable of understanding spoken and written language. We’re committed to supporting and inspiring developers and engineers from all walks of life.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

AWS Machine Learning Blog

JANUARY 17, 2024

About the Authors Xin Huang is a Senior Applied Scientist for Amazon SageMaker JumpStart and Amazon SageMaker built-in algorithms. His research interests are in the area of natural language processing, explainable deep learning on tabular data, and robust analysis of non-parametric space-time clustering.

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AWS Python Machine Learning Machine Learning 131

Snorkel AI

MARCH 21, 2023

In 2018, we did a piece of research where we tried to estimate the value of AI and machine learning across geographies, across use cases, and across sectors. One is compared to our first survey conducted in 2018, we see more enterprises investing in AI capability. We need data scientists familiar with deep learning frameworks.

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Explosion

NOVEMBER 21, 2019

from_disk("/path/to/s2v_reddit_2015_md") nlp.add_pipe(s2v) doc = nlp("A sentence about natural language processing.") text == "natural language processing" freq = doc[3:6]._.s2v_freq While few data scientists would endorse this as best practice, the qualitative evaluation does have important advantages.

Natural Language Processing 52

Natural Language Processing Data Scientist Machine Learning Machine Learning 52

Explosion

MARCH 17, 2019

of the spaCy Natural Language Processing library includes a huge number of features, improvements and bug fixes. spaCy is an open-source library for industrial-strength natural language processing in Python. Maybe you’re a grad student working on a paper, maybe you’re a data scientist working on a prototype.

Python 52

Python Natural Language Processing Deep Learning Deep Learning 52

Snorkel AI

MAY 25, 2023

Large language models are foundation models (a kind of large neural network) that generate or embed text. The text they generate can be conditioned by giving them a starting point or “prompt,” enabling them to solve useful tasks expressed in natural language or code. However, modern large language models make it even easier.

Natural Language Processing 52

Natural Language Processing Python Machine Learning Machine Learning 52

Snorkel AI

MAY 25, 2023

Large language models are foundation models (a kind of large neural network) that generate or embed text. The text they generate can be conditioned by giving them a starting point or “prompt,” enabling them to solve useful tasks expressed in natural language or code. However, modern large language models make it even easier.

Natural Language Processing 52

Natural Language Processing Python Machine Learning Machine Learning 52

Snorkel AI

APRIL 25, 2023

What the plot here on the X-axis is showing is just, from 2018 up to 2022, relative performance from the best system that we had because of a good benchmark like MLPerf and seeing what the improvement was with respect to “Moore’s Law” (because Moore’s law has been slowing now). Now, these are the “fundamental benchmarks.”

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

APRIL 25, 2023

What the plot here on the X-axis is showing is just, from 2018 up to 2022, relative performance from the best system that we had because of a good benchmark like MLPerf and seeing what the improvement was with respect to “Moore’s Law” (because Moore’s law has been slowing now). Now, these are the “fundamental benchmarks.”

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Hacker News

JULY 1, 2025

LinkedIn: https://www.linkedin.com/in/edwin-genego/ Throughout my career since 2018 I have primarily been a Python and Django developer, with a unintentional pivot to AI integrations & engineering in 2022/2023. I'm a data scientist, I'm looking for hard problems to solve.

Python 63

Python AWS SQL ML 63

AWS Machine Learning Blog

OCTOBER 21, 2024

About the Authors Maira Ladeira Tanke is a Senior Generative AI Data Scientist at AWS. Her work spans speech recognition, natural language processing, and large language models. Andrew Gordon Wilson before joining Amazon in 2018.

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AWS Machine Learning Blog

NOVEMBER 11, 2024

Data scientists and developers can quickly prototype and experiment with various ML use cases, accelerating the development and deployment of ML applications. This fine-tuning process involves providing the model with a dataset specific to the target domain. Domain adaption format You can fine-tune the Meta Llama 3.2

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