AWS Machine Learning Blog

SEPTEMBER 10, 2024

Improvements using foundation models Despite yielding promising results, PORPOISE and HEEC algorithms use backbone architectures trained using supervised learning (for example, ImageNet pre-trained ResNet50). Tamas helped customers in the Healthcare and Life Science vertical to innovate through the adoption of Machine Learning.

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How to Learn Machine Learning

MAY 14, 2023

Acquiring Essential Machine Learning Knowledge Once you have a strong foundation in mathematics and programming, it’s time to dive into the world of machine learning. Additionally, you should familiarize yourself with essential machine learning concepts such as feature engineering, model evaluation, and hyperparameter tuning.

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IBM Journey to AI blog

JULY 6, 2023

That’s where data science comes in. The term data science was first used in the 1960s when it was interchangeable with the phrase “computer science.” ” “Data science” was first used as an independent discipline in 2001.

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AWS Machine Learning Blog

OCTOBER 30, 2024

It was distilled from a larger teacher model (approximately 5 billion parameters), which was pre-trained on a large amount of unlabeled ASIN data and pre-fine-tuned on a set of Amazon supervised learning tasks (multi-task pre-fine-tuning). Kara is passionate about innovation and continuous learning.

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Heartbeat

OCTOBER 25, 2023

Empowering Data Scientists and Machine Learning Engineers in Advancing Biological Research Image from European Bioinformatics Institute Introduction: In biological research, the fusion of biology, computer science, and statistics has given birth to an exciting field called bioinformatics.

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Pickl AI

AUGUST 1, 2024

Caffe: A Deep Learning framework focused on speed and modularity, often used for image processing tasks. MXNet: An efficient and flexible Deep Learning framework that supports multiple programming languages and is particularly well-suited for cloud computing.

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Pickl AI

SEPTEMBER 23, 2024

Understanding Data Science Data Science is a multidisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines principles from statistics, mathematics, computer science, and domain-specific knowledge to analyse and interpret complex data.

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