Remove 2015 Remove Data Science Remove Natural Language Processing
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Counting shots, making strides: Zero, one and few-shot learning unleashed 

Data Science Dojo

For instance, in natural language processing, a model trained on various languages might be tasked with translating a language it has never seen before. This comprehensive evaluation sheds light on the landscape of zero-shot learning methodologies, exploring the strengths and challenges across various approaches.

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Evolving Trends in Data Science: Insights from ODSC Conference Sessions from 2015 to 2024

ODSC - Open Data Science

Over the past decade, data science has undergone a remarkable evolution, driven by rapid advancements in machine learning, artificial intelligence, and big data technologies. This blog dives deep into these changes of trends in data science, spotlighting how conference topics mirror the broader evolution of datascience.

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Origins of Generative AI and Natural Language Processing with ChatGPT

ODSC - Open Data Science

2000–2015 The new millennium gave us low-rise jeans, trucker hats, and bigger advancements in language modeling, word embeddings, and Google Translate. 2015 and beyond — Word2vec, GloVe, and FASTTEXT Word2vec, GloVe, and FASTTEXT focused on word embeddings or word vectorization. or ChatGPT (2022) ChatGPT is also known as GPT-3.5

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Evolution of GPT series: The GPT revolution from 1 to 4 trillion

Data Science Dojo

Deep learning And NLP Deep Learning and Natural Language Processing (NLP) are like best friends in the world of computers and language. Building Chatbots involves creating AI systems that employ deep learning techniques and natural language processing to simulate natural conversational behavior.

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Two Indispensable Tools for Measuring the Quality of AI Systems

ODSC - Open Data Science

For example, we often have high volumes of objective data we can use to measure the quality of a model that predicts who might have diabetes. About the Author/ODSC East 2025 Speaker: David Mack is a Principal Data Scientist within Humana’s Enterprise AI organization.

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MLOps and the evolution of data science

IBM Journey to AI blog

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning. How MLOps will be used within the organization.

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Machine Learning and Language (ML²) at CDS: Moving NLP Forward

NYU Center for Data Science

It’s a pivotal time in Natural Language Processing (NLP) research, marked by the emergence of large language models (LLMs) that are reshaping what it means to work with human language technologies. Cho’s work on building attention mechanisms within deep learning models has been seminal in the field.