Towards AI

NOVEMBER 16, 2024

In particular, min-max optimisation is curcial for GANs [2], statistics, online learning [6], deep learning, and distributed computing [7]. Vishnoi, “A convergent and dimension-independent first-order algorithm for min-maxoptimization,” arXiv preprint arXiv:2006.12376, 2020.[2] 139–144, 2020.[3] 4880–4889, 2020.[9]

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Dataconomy

FEBRUARY 26, 2024

GPUs: The versatile powerhouses Graphics Processing Units, or GPUs, have transcended their initial design purpose of rendering video game graphics to become key elements of Artificial Intelligence (AI) and Machine Learning (ML) efforts.

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NYU Center for Data Science

SEPTEMBER 28, 2023

Building on this momentum is a dynamic research group at the heart of CDS called the Machine Learning and Language (ML²) group. By 2020, ML² was a thriving community, primarily known for its recurring speaker series where researchers presented their work to peers. What does it mean to work in NLP in the age of LLMs?

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Dataconomy

AUGUST 22, 2024

Aleksandr Timashov is an ML Engineer with over a decade of experience in AI and Machine Learning. On these projects, I mentored numerous ML engineers, fostering a culture of innovation within Petronas. You told us you were implementing these projects in 2020-2022, so it all started amid the Covid-19 times.

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Towards AI

APRIL 6, 2023

Coined after the viral phrase, ‘you only live once’ (YOLO), the machine learning (ML) world first coined this acronym and repurposed it to You Only Look Once — YOLO. YOLOv1 was devised as a deep learning architecture optimized for fast object detection.

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Heartbeat

AUGUST 1, 2023

A World of Computer Vision Outside of Deep Learning Photo by Museums Victoria on Unsplash IBM defines computer vision as “a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs [1].”

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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.

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AWS Machine Learning Blog

DECEMBER 14, 2023

In line with this mission, Talent.com collaborated with AWS to develop a cutting-edge job recommendation engine driven by deep learning, aimed at assisting users in advancing their careers. This can significantly shorten the time needed to deploy the Machine Learning (ML) pipeline to production. session.Session().region_name

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Heartbeat

MAY 23, 2023

As technology continues to improve exponentially, deep learning has emerged as a critical tool for enabling machines to make decisions and predictions based on large volumes of data. Edge computing may change how we think about deep learning. Standardizing model management can be tricky but there is a solution.

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AWS Machine Learning Blog

SEPTEMBER 9, 2024

To address customer needs for high performance and scalability in deep learning, generative AI, and HPC workloads, we are happy to announce the general availability of Amazon Elastic Compute Cloud (Amazon EC2) P5e instances, powered by NVIDIA H200 Tensor Core GPUs. 48xlarge sizes through Amazon EC2 Capacity Blocks for ML.

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PyImageSearch

JANUARY 2, 2023

Figure 1: Global Funding in Health Tech Companies (source: Mrazek and O’Neill, 2020 ). This blog will cover the benefits, applications, challenges, and tradeoffs of using deep learning in healthcare. This series is about CV and DL for Industrial and Big Business Applications.

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Mlearning.ai

AUGUST 11, 2023

Introduction GPUs as main accelerators for deep learning training tasks suffer from under-utilization. Authors of AntMan [1] propose a deep learning infrastructure, which is a co-design of cluster schedulers (e.g., with deep learning frameworks (e.g., with deep learning frameworks (e.g.,

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Towards AI

AUGUST 25, 2023

How to get started with an AI project Vackground on Unsplash Background Here I am assuming that you have read my previous article on How to Learn AI. Machine learning (ML) is a subset of AI that provides computer systems the ability to automatically learn and improve from experience without being explicitly programmed.

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Towards AI

JULY 20, 2023

medium.com Talking about PyTorch… Basic Tutorials An awesome introduction to PyTorch showing an end-to-end ML pipeline from loading your data all the way to saving a trained model, includes a Colab notebook: Learn the Basics – PyTorch Tutorials 1.8.0 LineFlow was designed to use in all deep learning… github.com Repo Cypher ?‍?

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Applied Data Science

AUGUST 2, 2021

They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesis testing and deep learning to the team. Give this technique a try to take your team’s ML modelling to the next level.

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AWS Machine Learning Blog

MARCH 23, 2023

Leidos has partnered with AWS to develop an approach to privacy-preserving, confidential machine learning (ML) modeling where you build cloud-enabled, encrypted pipelines. In this post, we show how to activate privacy-preserving ML predictions for the most highly regulated environments. What is cryptographic computing?

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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.

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ODSC - Open Data Science

FEBRUARY 9, 2023

2020) When I wrote that statement a few years ago, I meant it mostly in the context of business concerns: a data scientist should have empathy for the needs and concerns of the people downstream who will consume the results of the models they build. Nina Zumel and John Mount, Practical Data Science with R, 2nd Ed.

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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.

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AWS Machine Learning Blog

FEBRUARY 23, 2023

Volume of corner cases – ML models need to handle a wide range of corner cases. SageMaker is a fully managed machine learning (ML) service. Data parallelism supports popular deep learning frameworks PyTorch, PyTorch Lightening, TensorFlow, and Hugging Face Transformers.

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AWS Machine Learning Blog

OCTOBER 10, 2023

Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. It is challenging to centralize such data for ML due to privacy requirements, high cost of data transfer, or operational complexity. The ML framework used at FL clients is TensorFlow.

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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 evaluated the WAPE for all BLs in the auto end market for 2019, 2020, and 2021.

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AWS Machine Learning Blog

JANUARY 13, 2023

Machine learning (ML) presents an opportunity to address some of these concerns and is being adopted to advance data analytics and derive meaningful insights from diverse HCLS data for use cases like care delivery, clinical decision support, precision medicine, triage and diagnosis, and chronic care management.

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Applied Data Science

OCTOBER 25, 2021

Solid theoretical background in statistics and machine learning, experience with state-of-the-art deep learning algorithms, expert command of tools for data pre-processing, database management and visualisation, creativity and story-telling abilities, communication and team-building skills, familiarity with the industry.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

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Mlearning.ai

APRIL 8, 2023

In this article, we’ll look at the evolution of these state-of-the-art (SOTA) models and algorithms, the ML techniques behind them, the people who envisioned them, and the papers that introduced them. 2017) “ BERT: Pre-training of deep bidirectional transformers for language understanding ” by Devlin et al.

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ML @ CMU

SEPTEMBER 15, 2023

2020) showed that TTA via reconstruction in slot-centric models fails due to a reconstruction segmentation trade-off: as the entity bottleneck loosens, there’s an improvement in reconstruction; however, segmentation subsequently deteriorates. In particular, Engelcke et al. We consider the following baselines: (i) Mask2Former (Cheng et al.,

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AWS Machine Learning Blog

NOVEMBER 22, 2023

For decades, Amazon has pioneered and innovated machine learning (ML), bringing delightful experiences to its customers. From the earliest days, Amazon has used ML for various use cases such as book recommendations, search, and fraud detection. About the Authors Abhinandan Patni is a Senior Software Engineer at Amazon Search.

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ODSC - Open Data Science

MAY 25, 2023

Here’s what you need to know: sktime is a Python package for time series tasks like forecasting, classification, and transformations with a familiar and user-friendly scikit-learn-like API. Build tuned auto-ML pipelines, with common interface to well-known libraries (scikit-learn, statsmodels, tsfresh, PyOD, fbprophet, and more!)

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Mlearning.ai

OCTOBER 14, 2023

Introduction Deep learning tasks usually demand high computation/memory requirements and their computations are embarrassingly parallel. The paper claims that distributed training has been facilitated by deep learning frameworks, but fault tolerance did not get enough attention.

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AWS Machine Learning Blog

JUNE 7, 2023

Starting June 7th, both Falcon LLMs will also be available in Amazon SageMaker JumpStart, SageMaker’s machine learning (ML) hub that offers pre-trained models, built-in algorithms, and pre-built solution templates to help you quickly get started with ML. Will Badr is a Sr.

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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. Each season consists of around 17,000 plays.

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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. Import the data loader into the training script.

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Defined.ai blog

OCTOBER 9, 2022

Fight sophisticated cyber attacks with AI and ML When “virtual” became the standard medium in early 2020 for business communications from board meetings to office happy hours, companies like Zoom found themselves hot in demand. There is also concern that attackers are using AI and ML technology to launch smarter, more advanced attacks.

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DrivenData Labs

JUNE 14, 2023

His research interest is deep metric learning and computer vision. Prior to Baidu, he was a Research Intern in Baidu Research from 2021 to 2022 and a Remote Research Intern in Inception Institute of Artificial Intelligence from 2020 to 2021. His research interests focus on deep representation learning, data problem (e.g.,

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Snorkel AI

FEBRUARY 6, 2023

You could imagine, for deep learning, you need, really, a lot of examples. So, deep learning, similarity search is a very easy, simple, task. We’ll solve this with self-supervised learning, which is basically the [research] area catching on fire since 2020 onward when Google released the SimCLR.

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Snorkel AI

FEBRUARY 6, 2023

You could imagine, for deep learning, you need, really, a lot of examples. So, deep learning, similarity search is a very easy, simple, task. We’ll solve this with self-supervised learning, which is basically the [research] area catching on fire since 2020 onward when Google released the SimCLR.

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