Heartbeat

FEBRUARY 6, 2023

How do Object Detection Algorithms Work? There are two main categories of object detection algorithms. Two-Stage Algorithms: Two-stage object detection algorithms consist of two different stages. Single-stage object detection algorithms do the whole process through a single neural network model.

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ODSC - Open Data Science

MARCH 12, 2025

By analyzing conference session titles and abstracts from 2018 to 2024, we can trace the rise and fall of key trends that shaped the industry. The Decline of Traditional MachineLearning 20182020: Algorithms like random forests, SVMs, and gradient boosting were frequent discussion points.

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Flipboard

MAY 29, 2025

Custom geospatial machine learning : Fine-tune a specialized regression, classification, or segmentation model for geospatial machine learning (ML) tasks. A common high-performance search algorithm for this is approximate nearest neighbor (ANN). The general solution flow is shown in the following diagram.

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Flipboard

JANUARY 14, 2025

His work on the attention mechanism and latent variable models received an Outstanding Paper Award at ACL 2017 and the Best Paper Award for JNLP in 2018 and 2019. She has industry experience, multiple first-author publications in top ML/NLP conferences, and strong foundation in mathematics and algorithms. She obtained her Ph.D.

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Dataconomy

MARCH 19, 2025

Developed by Google, these devices are application-specific integrated circuits (ASICs) that enhance the performance of AI algorithms, particularly for tasks related to neural networks and deep learning. Their architecture is less suited to the large-scale matrix operations that are typical in modern ML applications.

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ML @ CMU

DECEMBER 29, 2023

These factors introduce noise that can affect hyperparameter tuning algorithms and lead to suboptimal model selection. Traditional distributed ML assumes each worker/client has a random (identically distributed) sample of the training data. that are fed into an FL training algorithm (more details in the next section).

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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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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.

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ODSC - Open Data Science

SEPTEMBER 21, 2023

Secondly, to be a successful ML engineer in the real world, you cannot just understand the technology; you must understand the business. Some typical examples are given in the following table, along with some discussion as to whether or not ML would be an appropriate tool for solving the problem: Figure 1.1:

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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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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). In short, Golang is not widely used for exploratory data science, but rewriting your algorithms in Golang might be a good idea. Golang Data Science Books. Thoughts from the Community.

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AWS Machine Learning Blog

FEBRUARY 29, 2024

is a startup dedicated to the mission of democratizing artificial intelligence technologies through algorithmic and software innovations that fundamentally change the economics of deep learning. Graviton Technical Guide is a good resource to consider while evaluating your ML workloads to run on Graviton. Founded in 2021, ThirdAI Corp.

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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. NLP algorithms help computers understand, interpret, and generate natural language.

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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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DataRobot Blog

APRIL 1, 2018

Today’s data management and analytics products have infused artificial intelligence (AI) and machine learning (ML) algorithms into their core capabilities. 2] Gartner, Five Reasons to Begin Converging Application and Data Integration , Published: 12 March 2015 Refreshed: 05 February 2018, Analyst(s): Eric Thoo | Keith Guttridge. [3]

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AWS Machine Learning Blog

MAY 2, 2023

JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. 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).

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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.

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Heartbeat

MARCH 14, 2023

By incorporating computer vision methods and algorithms into robots, they are able to view and understand their environment. Object recognition and tracking algorithms include the CamShift algorithm , Kalman filter , and Particle filter , among others.

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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. In this post, we deep dive into the technical details of this ML model.

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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 also demonstrate the performance of our state-of-the-art point cloud-based product lifecycle prediction algorithm.

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AWS Machine Learning Blog

JULY 13, 2023

Another way can be to use an AllReduce algorithm. For example, in the ring-allreduce algorithm, each node communicates with only two of its neighboring nodes, thereby reducing the overall data transfers. Train a binary classification model using the SageMaker built-in XGBoost algorithm. PMLR, 2018. [2] 3] Dai, Wei, et al.

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AWS Machine Learning Blog

JANUARY 17, 2024

SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying your ML models. It will show up when you when you choose Train.

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O'Reilly Media

APRIL 5, 2023

That’s because AI algorithms are trained on data. And it’s safe to say that most AI algorithms are trained on datasets that are significantly older. In 2018, Reuters reported that Amazon had scrapped an AI recruiting tool that had developed a bias against female applicants. You turned left or right. Your coat was red or blue.

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AWS Machine Learning Blog

MAY 31, 2023

In 2018, BERT-large made its debut with its 340 million parameters and innovative transformer architecture, setting the benchmark for performance on NLP tasks. Recent advances in ML have given rise to a new class of models known as foundation models , which have billions of parameters and are trained on massive amounts of data.

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AWS Machine Learning Blog

JANUARY 13, 2023

It involves training a global machine learning (ML) model from distributed health data held locally at different sites. The eICU data is ideal for developing ML algorithms, decision support tools, and advancing clinical research. Training ML models with a single data point at a time is tedious and time-consuming.

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AWS Machine Learning Blog

APRIL 3, 2023

Causal inference Causality is all about understanding change, but how to formalize this in statistics and machine learning (ML) is not a trivial exercise. Note that this solution is currently available in the US West (Oregon) Region only. In this crop yield study, the nitrogen added as fertilizer and the yield outcomes might be confounded.

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AWS Machine Learning Blog

AUGUST 16, 2023

Training machine learning (ML) models to interpret this data, however, is bottlenecked by costly and time-consuming human annotation efforts. Our solution is based on the DINO algorithm and uses the SageMaker distributed data parallel library (SMDDP) to split the data over multiple GPU instances. tif" --include "_B03.tif"

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Mlearning.ai

MAY 5, 2023

These models, which are based on artificial intelligence and machine learning algorithms, are designed to process vast amounts of natural language data and generate new content based on that data. It wasn’t until the development of deep learning algorithms in the 2000s and 2010s that LLMs truly began to take shape.

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Towards AI

JULY 19, 2023

The Continuing Story of Neural Magic Around New Year’s time, I pondered about the upcoming sparsity adoption and its consequences on inference w/r/t ML models. But first… a word from our sponsors: [link] If you enjoy the read, help us out by giving it a ?? and share with friends! The company is Neural Magic. Follow their code on GitHub.

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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. Machine learning algorithms rely heavily on the data they are trained on. Unfortunately, the algorithm was trained on resumes predominantly submitted by men over 10 years. Why Does Data Quality Matter? Sounds great, right?

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AWS Machine Learning Blog

NOVEMBER 8, 2023

The MONAI AI models and applications can be hosted on Amazon SageMaker , which is a fully managed service to deploy machine learning (ML) models at scale. When there are no requests to process, this deployment option can downscale the instance count to zero for cost savings, which is ideal for medical imaging ML inference workloads.

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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., Understanding the robustness of image segmentation algorithms to adversarial attacks is critical for ensuring their reliability and security in practical applications.

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AWS Machine Learning Blog

SEPTEMBER 23, 2024

Since its launch in 2018, Just Walk Out technology by Amazon has transformed the shopping experience by allowing customers to enter a store, pick up items, and leave without standing in line to pay. AI model training—in which curated data is fed to selected algorithms—helps the system refine itself to produce accurate results.

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Google Research AI blog

FEBRUARY 3, 2023

As part of Google’s mission to help people access trusted information in critical moments, we use satellite imagery and machine learning (ML) to track wildfires and inform affected communities. Smoke plumes obscuring the 2018 Camp Fire in California. Our wildfire tracker was recently expanded. μm and 11.2

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Flipboard

NOVEMBER 24, 2023

Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. Clay Elmore is an AI/ML Specialist Solutions Architect at AWS. He has worked on ML applications in many different industries ranging from energy trading to hospitality marketing.

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Mlearning.ai

MARCH 15, 2023

Emergence Google coined the term federated learning (FL) during the Cambridge Analytical scandal of 2018. The ML model is then used by the user through an API by sending a request to access a specific feature. The aggregated updates are used to create an improved central ML model.

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IBM Journey to AI blog

MARCH 4, 2024

In the four years since it burst onto the market, 5G has been widely touted as a disruptive technology, capable of transformation on a similar scale to artificial intelligence (AI) , the Internet of Things (IoT) and machine learning (ML). Smart factories: With AI and ML, factories everywhere are already becoming smarter and more efficient.

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