ODSC - Open Data Science

SEPTEMBER 21, 2023

The majority of us who work in machine learning, analytics, and related disciplines do so for organizations with a variety of different structures and motives. The following is an extract from Andrew McMahon’s book , Machine Learning Engineering with Python, Second Edition.

Machine Learning 98

Machine Learning Machine Learning ML ML 98

ML @ CMU

JANUARY 2, 2025

2018 ) to enhance training (see Materials and Methods in Zhang et al., It then outputs the estimated (Q_t) for this action, trained through the temporal-difference error (TD error) after receiving the reward (r_t) ((|r_t+gamma Q_{t+1}-Q_{t}|), where (gamma) denotes the temporal discount factor).

Deep Learning 336

Deep Learning Deep Learning AI AI 336

Hacker News

JANUARY 26, 2024

As the number of accepted papers at AI and ML conferences reaches into the thousands, it has become unclear how researchers access and read research publications. In this paper, we investigate the role of social media influencers in enhancing the visibility of machine learning research, particularly the citation counts of papers they share.

Machine Learning 139

Machine Learning Machine Learning ML ML 139

AWS Machine Learning Blog

APRIL 7, 2025

This approach allows for greater flexibility and integration with existing AI and machine learning (AI/ML) workflows and pipelines. By providing multiple access points, SageMaker JumpStart helps you seamlessly incorporate pre-trained models into your AI/ML development efforts, regardless of your preferred interface or workflow.

AWS 89

AWS Machine Learning Machine Learning ML 89

AWS Machine Learning Blog

AUGUST 29, 2024

The AWS DeepRacer League is the world’s first autonomous racing league, open to everyone and powered by machine learning (ML). AWS DeepRacer brings builders together from around the world, creating a community where you learn ML hands-on through friendly autonomous racing competitions.

AWS 121

AWS ML ML Machine Learning 121

AWS Machine Learning Blog

MARCH 8, 2023

MPII is using a machine learning (ML) bid optimization engine to inform upstream decision-making processes in power asset management and trading. MPII’s bid optimization engine solution uses ML models to generate optimal bids for participation in different markets. Data comes from disparate sources in a number of formats.

AWS 95

AWS Machine Learning Machine Learning Analytics 95

ODSC - Open Data Science

APRIL 28, 2023

Be sure to check out her talk, “ Power trusted AI/ML Outcomes with Data Integrity ,” there! Due to the tsunami of data available to organizations today, artificial intelligence (AI) and machine learning (ML) are increasingly important to businesses seeking competitive advantage through digital transformation.

ML 98

ML ML Data Silos Data Quality 98

AWS Machine Learning Blog

MARCH 30, 2023

Not only was he widely considered the top-rated goalkeeper in the league during the 2021/22 season, but he also held that title back in 2018/19 when Eintracht Frankfurt reached the Europa League semifinals. The result is a machine learning (ML)-powered insight that allows fans to easily evaluate and compare the goalkeepers’ proficiencies.

Machine Learning 92

Machine Learning Machine Learning AWS ML 92

AWS Machine Learning Blog

FEBRUARY 8, 2023

Machine learning (ML) has become ubiquitous. Our customers are employing ML in every aspect of their business, including the products and services they build, and for drawing insights about their customers. To build an ML-based application, you have to first build the ML model that serves your business requirement.

Machine Learning 99

Machine Learning Machine Learning ML ML 99

Dataconomy

JUNE 30, 2021

Big data is one of the most rapidly growing industries in the world and was valued at $169 billion in 2018, with expectations to approach the $300 billion mark by the end of next year. Even with such monetary influence in the world already, the industry is still figuring itself.

Predictive Analytics 195

Predictive Analytics Analytics Analytics Big Data 195

Iguazio

SEPTEMBER 20, 2023

20 Newsgroups A dataset containing roughly 20,000 newsgroup documents spanning a variety of topics, for text classification, text clustering and similar ML applications. million articles from 20,000 news sources across a seven day period in 2017 and 2018. Long-Form Content 14. The newsgroups are: comp.graphics, comp.os.ms-windows.misc,

Machine Learning 94

Machine Learning Machine Learning Database Data Scientist 94

ODSC - Open Data Science

MARCH 22, 2023

Be sure to check out his session, “ Improving ML Datasets with Cleanlab, a Standard Framework for Data-Centric AI ,” there! Anybody who has worked on a real-world ML project knows how messy data can be. Everybody knows you need to clean your data to get good ML performance. A common gripe I hear is: “Garbage in, garbage out.

ML 88

ML ML Data Scientist AI 88

Towards AI

NOVEMBER 16, 2024

This blog explores how Keswani’s method addresses common challenges in min-max scenarios, with applications in areas of modern Machine Learning such as GANs, adversarial training, and distributed computing, providing a robust alternative to traditional algorithms like Gradient Descent Ascent (GDA). Daskalakis and I. Mescheder, S.

Algorithm 105

Algorithm ML ML AI 105

Google Research AI blog

MAY 15, 2023

This became apparent in 2018, when the Gender Shades study highlighted that computer vision systems struggled to detect people with darker skin tones, and performed particularly poorly for women with darker skin tones. How do we effectively annotate skin tone for use in inclusive machine learning (ML)?

ML 109

ML ML Machine Learning Machine Learning 109

DagsHub

JUNE 3, 2024

In 2018, there were extensive news reports that an Uber self-driving car made an accident with a pedestrian in Tempe, Arizona. The pedestrian died, and investigators found that there was an issue with the machine learning (ML) model in the car, so it failed to identify the pedestrian beforehand.

Machine Learning 52

Machine Learning Machine Learning Decision Trees ML 52

Flipboard

APRIL 13, 2023

The seeds of a machine learning (ML) paradigm shift have existed for decades, but with the ready availability of scalable compute capacity, a massive proliferation of data, and the rapid advancement of ML technologies, customers across industries are transforming their businesses.

AWS 182

AWS AI ML ML 182

Flipboard

FEBRUARY 2, 2023

With advanced analytics derived from machine learning (ML), the NFL is creating new ways to quantify football, and to provide fans with the tools needed to increase their knowledge of the games within the game of football. We then explain the details of the ML methodology and model training procedures.

Cross Validation 123

Cross Validation ML ML Machine Learning 123

AWS Machine Learning Blog

FEBRUARY 6, 2024

To support overarching pharmacovigilance activities, our pharmaceutical customers want to use the power of machine learning (ML) to automate the adverse event detection from various data sources, such as social media feeds, phone calls, emails, and handwritten notes, and trigger appropriate actions.

AWS 125

AWS ML ML Data Preparation 125

Data Science 101

APRIL 26, 2019

Go Machine Learning Projects (2018) – this book uses gonum and gorgonia in the examples Machine Learning with Go (2017). Machine Learning with Go? Golang Data Science Books. There have even been a couple books written about the topic. Thoughts from the Community.

Data Science 111

Data Science Machine Learning Machine Learning Python 111

How to Learn Machine Learning

OCTOBER 10, 2024

The quality of your training data in Machine Learning (ML) can make or break your entire project. This article explores real-world cases where poor-quality data led to model failures, and what we can learn from these experiences. Machine learning algorithms rely heavily on the data they are trained on.

Machine Learning 52

Machine Learning Machine Learning Data Quality Algorithm 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. These models are then pushed to an Amazon Simple Storage Service (Amazon S3) bucket using DVC, a version control tool for ML models. Business requirements We are the US squad of the Sportradar AI department.

ML 93

ML ML Deep Learning Deep Learning 93

AWS Machine Learning Blog

SEPTEMBER 19, 2023

Quantitative modeling and forecasting – Generative models can synthesize large volumes of financial data to train machine learning (ML) models for applications like stock price forecasting, portfolio optimization, risk modeling, and more. Multi-modal models that understand diverse data sources can provide more robust forecasts.

AWS 130

AWS AI AI ML 130

AWS Machine Learning Blog

MAY 14, 2024

An important aspect of developing effective generative AI application is Reinforcement Learning from Human Feedback (RLHF). RLHF is a technique that combines rewards and comparisons, with human feedback to pre-train or fine-tune a machine learning (ML) model. RHLF-custom-feedback":{"workerId":"private.us-east-1.8c185c045aed3bef","result":{"relevance":{"label":"5

AWS 119

AWS AI AI Machine Learning 119

Heartbeat

SEPTEMBER 13, 2023

This guide will buttress explainability in machine learning and AI systems. The explainability concept involves providing insights into the decisions and predictions made by artificial intelligence (AI) systems and machine learning models. What is Explainability?

Machine Learning 52

Machine Learning Machine Learning AI AI 52

AWS Machine Learning Blog

APRIL 6, 2023

& AWS Machine Learning Solutions Lab (MLSL) Machine learning (ML) is being used across a wide range of industries to extract actionable insights from data to streamline processes and improve revenue generation. We trained three models using data from 2011–2018 and predicted the sales values until 2021.

Machine Learning 104

Machine Learning Machine Learning ML ML 104

Mlearning.ai

FEBRUARY 10, 2023

Techniques for reducing avoidable bias If you train your machine learning model and you see that your algorithm is suffering from high avoidable bias, you could the following techniques to reduce it. Summary Bias and variance are two main sources of error in machine learning. Machine learning yearning.

Machine Learning 52

Machine Learning Machine Learning Deep Learning Deep Learning 52

Mlearning.ai

JUNE 8, 2023

Netflix-style if-you-like-these-movies-you’ll-like-this-one-too) All kinds of search Text search (like Google Search) Image search (like Google Reverse Image Search) Chatbots and question-answering systems Data preprocessing (preparing data to be fed into a machine learning model) One-shot/zero-shot learning (i.e.

Machine Learning 52

Machine Learning Machine Learning Clustering Database 52

AWS Machine Learning Blog

JULY 9, 2024

of its consolidated revenues during the years ended December 31, 2019, 2018 and 2017, respectively. Sonnet within 24 hours.” – Diana Mingels, Head of Machine Learning at Kensho. About the authors Qingwei Li is a Machine Learning Specialist at Amazon Web Services. The benchmark shows that Anthropic Claude 3.5

AWS 126

AWS AI AI Machine Learning 126

Tableau

DECEMBER 1, 2021

Even modern machine learning applications should use visual encoding to explain data to people. April 2018), which focused on users who do understand joins and curating federated data sources. May 2017), which was Tableau’s first exploration of Machine Learning (ML) technology to provide computer assistance.

Tableau 145

Tableau ML ML Database 145

Towards AI

JULY 20, 2023

It’s also an area that stands to benefit most from automated or semi-automated machine learning (ML) and natural language processing (NLP) techniques. Over the past several years, researchers have increasingly attempted to improve the data extraction process through various ML techniques. This study by Bui et al.

Natural Language Processing 98

Natural Language Processing ML ML Support Vector Machines 98

Heartbeat

JANUARY 29, 2024

Photo by Brett Jordan on Unsplash In the ever-evolving landscape of artificial intelligence and machine learning, researchers and practitioners continuously seek to elevate the capabilities of intelligent systems. Among the myriad breakthroughs in this field, Meta-Learning is pushing the boundaries of machine learning.

Machine Learning 52

Machine Learning Machine Learning Natural Language Processing Algorithm 52

AWS Machine Learning Blog

FEBRUARY 29, 2024

We are actively working on extending our methods to additional domains, such as computer vision, but be aware that our efficiency improvements do not translate to all ML domains at this time. Graviton Technical Guide is a good resource to consider while evaluating your ML workloads to run on Graviton.

AWS 131

AWS Deep Learning Deep Learning ML 131

AWS Machine Learning Blog

JANUARY 13, 2023

To mitigate these challenges, we propose a federated learning (FL) framework, based on open-source FedML on AWS, which enables analyzing sensitive HCLS data. It involves training a global machine learning (ML) model from distributed health data held locally at different sites.

AWS 91

AWS Analytics Analytics Machine Learning 91

AWS Machine Learning Blog

JUNE 26, 2024

This blog is part of the series, Generative AI and AI/ML in Capital Markets and Financial Services. Prompt the agent to build an optimal portfolio using the collected data What are the closing prices of stocks AAAA, WWW, DDD in year 2018? Sovik has published articles and holds a patent in ML model monitoring.

AWS 130

AWS AI AI Database 130

ML @ CMU

DECEMBER 29, 2023

Federated Learning: An Overview In federated learning (FL), user data remains on the device and only model updates are communicated. Source: Wikipedia) C ross-device federated learning (FL) is a machine learning setting that considers training a model over a large heterogeneous network of devices such as mobile phones or wearables.

Algorithm 209

Algorithm Machine Learning Machine Learning ML 209

ODSC - Open Data Science

APRIL 17, 2023

Source Purpose of Using DevSecOps in Traditional and ML Applications The DevSecOps practices are different in traditional and ML applications as each comes with different challenges. The characteristics which we saw for DevSecOps for traditional applications also apply to ML-based applications.

Data Science 97

Data Science ML ML Deep Learning 97

AWS Machine Learning Blog

FEBRUARY 10, 2023

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. Journal of machine learning research 9, no.

ML 85

ML ML Machine Learning Machine Learning 85