2018, Clustering and Natural Language Processing

From Rulesets to Transformers: A Journey Through the Evolution of SOTA in NLP

Mlearning.ai

APRIL 8, 2023

Charting the evolution of SOTA (State-of-the-art) techniques in NLP (Natural Language Processing) over the years, highlighting the key algorithms, influential figures, and groundbreaking papers that have shaped the field. Evolution of NLP Models To understand the full impact of the above evolutionary process.

Natural Language Processing

Natural Language Processing Algorithm Machine Learning Machine Learning

Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

AWS Machine Learning Blog

OCTOBER 5, 2023

Our high-level training procedure is as follows: for our training environment, we use a multi-instance cluster managed by the SLURM system for distributed training and scheduling under the NeMo framework. From 2015–2018, he worked as a program director at the US NSF in charge of its big data program. Youngsuk Park is a Sr.

AWS

AWS Machine Learning Machine Learning Deep Learning

Introduction to Autoencoders

Flipboard

JULY 10, 2023

By using our mathematical notation, the entire training process of the autoencoder can be written as follows: Figure 2 demonstrates the basic architecture of an autoencoder: Figure 2: Architecture of Autoencoder (inspired by Hubens, “Deep Inside: Autoencoders,” Towards Data Science , 2018 ).

Deep Learning

Deep Learning Deep Learning Machine Learning Machine Learning

Webinars

What’s New in Apache Airflow® 3.0—And How Will It Reshape Your Data Workflows?

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NLP in Legal Discovery: Unleashing Language Processing for Faster Case Analysis

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

Natural Language Processing Algorithm Artificial Intelligence Artificial Intelligence

Identifying defense coverage schemes in NFL’s Next Gen Stats

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. As an example, in the following figure, we separate Cover 3 Zone (green cluster on the left) and Cover 1 Man (blue cluster in the middle). Each season consists of around 17,000 plays.

ML

ML ML Machine Learning Machine Learning

A review of purpose-built accelerators for financial services

AWS Machine Learning Blog

SEPTEMBER 11, 2024

PBAs, such as graphics processing units (GPUs), have an important role to play in both these phases. The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference. With Inf1, they were able to reduce their inference latency by 25%, and costs by 65%.

AWS

AWS ML ML Clustering

How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

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. Then we needed to Dockerize the application, write a deployment YAML file, deploy the gRPC server to our Kubernetes cluster, and make sure it’s reliable and auto scalable. We recently developed four more new models.

ML

ML ML Deep Learning Deep Learning

Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart

AWS Machine Learning Blog

MAY 2, 2023

There are a few limitations of using off-the-shelf pre-trained LLMs: They’re usually trained offline, making the model agnostic to the latest information (for example, a chatbot trained from 2011–2018 has no information about COVID-19). They’re mostly trained on general domain corpora, making them less effective on domain-specific tasks.

Algorithm

Algorithm Machine Learning Machine Learning Natural Language Processing

A Comprehensive Guide to the main components of Big Data

Pickl AI

DECEMBER 2, 2024

According to a report by Statista, the global data sphere is expected to reach 180 zettabytes by 2025 , a significant increase from 33 zettabytes in 2018. Processing frameworks like Hadoop enable efficient data analysis across clusters. Frequently Asked Questions What is the Role of Data Processing Frameworks in Big Data?

Big Data

Big Data Big Data Data Lakes Apache Hadoop

A Comprehensive Guide to the Main Components of Big Data

Pickl AI

NOVEMBER 25, 2024

According to a report by Statista, the global data sphere is expected to reach 180 zettabytes by 2025 , a significant increase from 33 zettabytes in 2018. Processing frameworks like Hadoop enable efficient data analysis across clusters. Frequently Asked Questions What is the Role of Data Processing Frameworks in Big Data?

Big Data

Big Data Big Data Data Lakes Apache Hadoop

Robustness of a Markov Blanket Discovery Approach to Adversarial Attack in Image Segmentation: An…

Mlearning.ai

MARCH 9, 2023

Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al., 2018; Sitawarin et al., 2018; Papernot et al., 2018; Pang et al., 2012; Otsu, 1979; Long et al., For instance, Xu et al. Another study by Jin et al.

Deep Learning

Deep Learning Deep Learning Machine Learning Machine Learning

Against LLM maximalism

Explosion

MAY 17, 2023

A lot of people are building truly new things with Large Language Models (LLMs), like wild interactive fiction experiences that weren’t possible before. But if you’re working on the same sort of Natural Language Processing (NLP) problems that businesses have been trying to solve for a long time, what’s the best way to use them?

Supervised Learning

Supervised Learning Natural Language Processing Clustering Machine Learning

Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium

AWS Machine Learning Blog

JANUARY 17, 2024

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. He retired from EPFL in December 2016.nnIn Use the `split` function to split each sentence into a list of words.n- Nitin Eusebius is a Sr.

AWS

AWS Python Machine Learning Machine Learning

Google Research, 2022 & beyond: Research community engagement

Google Research AI blog

FEBRUARY 28, 2023

For example, supporting equitable student persistence in computing research through our Computer Science Research Mentorship Program , where Googlers have mentored over one thousand students since 2018 — 86% of whom identify as part of a historically marginalized group.

ML

ML ML Deep Learning Deep Learning

McKinsey QuantumBlack experts: exciting foundation model future

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. Firstly, what is the state of the industry?

ML

ML ML AI AI

Meet the Winners of the Youth Mental Health Narratives Challenge

DrivenData Labs

FEBRUARY 3, 2025

His research focuses on applying natural language processing techniques to extract information from unstructured clinical and medical texts, especially in low-resource settings. I love participating in various competitions involving deep learning, especially tasks involving natural language processing or LLMs.

Machine Learning

Machine Learning Machine Learning Data Science Natural Language Processing

Data Science Current

From Rulesets to Transformers: A Journey Through the Evolution of SOTA in NLP

Fast and cost-effective LLaMA 2 fine-tuning with AWS Trainium

Webinars

Trending Sources

Introduction to Autoencoders

Webinars

NLP in Legal Discovery: Unleashing Language Processing for Faster Case Analysis

Identifying defense coverage schemes in NFL’s Next Gen Stats

A review of purpose-built accelerators for financial services

How Sportradar used the Deep Java Library to build production-scale ML platforms for increased performance and efficiency

Question answering using Retrieval Augmented Generation with foundation models in Amazon SageMaker JumpStart

A Comprehensive Guide to the main components of Big Data

A Comprehensive Guide to the Main Components of Big Data

Robustness of a Markov Blanket Discovery Approach to Adversarial Attack in Image Segmentation: An…

Against LLM maximalism

Fine-tune and deploy Llama 2 models cost-effectively in Amazon SageMaker JumpStart with AWS Inferentia and AWS Trainium

Google Research, 2022 & beyond: Research community engagement

McKinsey QuantumBlack experts: exciting foundation model future

Meet the Winners of the Youth Mental Health Narratives Challenge

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