Towards AI

NOVEMBER 16, 2024

Keswani’s Algorithm introduces a novel approach to solving two-player non-convex min-max optimization problems, particularly in differentiable sequential games where the sequence of player actions is crucial. Keswani’s Algorithm: The algorithm essentially makes response function : maxy∈{R^m} f (.,

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Algorithm ML ML AI 104

Data Science 101

JANUARY 8, 2020

Artificial Intelligence has nearly as many definitions as people you ask. Here are two intriguing definitions I have heard. This is actually a pretty good definition, but it is always changing so it is hard to evaluate and compare. Thus, the following definition works better. What is Machine Learning (ML)?

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Dataconomy

MAY 16, 2023

Their expertise lies in designing algorithms, optimizing models, and integrating them into real-world applications. They possess a deep understanding of machine learning algorithms, data structures, and programming languages. They possess a unique blend of statistical expertise, programming skills, and domain knowledge.

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Data Science Dojo

SEPTEMBER 8, 2023

It replaces complex algorithms with neural networks, streamlining and accelerating the predictive process. Machine Learning and Deep Learning: The Power Duo Machine Learning (ML) and Deep Learning (DL) are two critical branches of AI that bring exceptional capabilities to predictive analytics. Goals To predict future events and trends.

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DrivenData Labs

MAY 12, 2025

After the challenge, the research team at NOAA and NCEI worked with one of the winners to implement an ensemble of the top two models, incorporating into NOAA's High Definition Geomagnetic Model (HDGM) and making the predictions publicly available in real-time. A sample frame is shown with the most likely species identified.

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Algorithm Machine Learning Machine Learning Deep Learning 130

AWS Machine Learning Blog

FEBRUARY 12, 2024

The Ranking team at Booking.com plays a pivotal role in ensuring that the search and recommendation algorithms are optimized to deliver the best results for their users. Essential ML capabilities such as hyperparameter tuning and model explainability were lacking on premises.

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ML ML AWS Machine Learning 137

Google Research AI blog

MAY 19, 2023

Posted by Natalia Ponomareva and Alex Kurakin, Staff Software Engineers, Google Research Large machine learning (ML) models are ubiquitous in modern applications: from spam filters to recommender systems and virtual assistants. Therefore, protecting the privacy of the training data is critical to practical, applied ML.

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AWS Machine Learning Blog

NOVEMBER 15, 2023

AutoML allows you to derive rapid, general insights from your data right at the beginning of a machine learning (ML) project lifecycle. Understanding up front which preprocessing techniques and algorithm types provide best results reduces the time to develop, train, and deploy the right model.

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Algorithm AWS ML ML 123

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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Algorithm Deep Learning Deep Learning ML 90

AWS Machine Learning Blog

JANUARY 31, 2025

Increasingly, FMs are completing tasks that were previously solved by supervised learning, which is a subset of machine learning (ML) that involves training algorithms using a labeled dataset. Foundation models (FMs) are used in many ways and perform well on tasks including text generation, text summarization, and question answering.

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Supervised Learning AWS Data Scientist ML 100

ML @ CMU

APRIL 23, 2025

The paper analyzes two families of self-improvement algorithms: one based on supervised fine-tuning (SFT) and one on reinforcement learning (RLHF). They develop a sound algorithm that identifies both causal relationships and selection mechanisms, demonstrating its effectiveness through experiments on both synthetic and real-world data.

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Towards AI

FEBRUARY 20, 2024

Beginner’s Guide to ML-001: Introducing the Wonderful World of Machine Learning: An Introduction Everyone is using mobile or web applications which are based on one or other machine learning algorithms. You might be using machine learning algorithms from everything you see on OTT or everything you shop online.

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AWS Machine Learning Blog

MAY 31, 2023

PyTorch is a machine learning (ML) framework based on the Torch library, used for applications such as computer vision and natural language processing. This provides a major flexibility advantage over the majority of ML frameworks, which require neural networks to be defined as static objects before runtime.

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ML ML AWS Natural Language Processing 121

Dataconomy

MARCH 27, 2023

However, while RPA and ML share some similarities, they differ in functionality, purpose, and the level of human intervention required. In this article, we will explore the similarities and differences between RPA and ML and examine their potential use cases in various industries. What is machine learning (ML)?

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ML ML Machine Learning Machine Learning 70

Dataconomy

MAY 8, 2025

As organizations increasingly rely on ML for decision-making, the need for robust monitoring practices has never been more significant. Open-source machine learning monitoring (OSMLM) encompasses the systems and methods aimed at overseeing and optimizing ML models that have been deployed.

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Machine Learning Machine Learning ML ML 113

Dataconomy

APRIL 24, 2025

The LLM playground is a dynamic environment designed for exploring and testing large language models (LLMs) and their applications within artificial intelligence (AI) and machine learning (ML). Features of the LLM playground One of the standout characteristics of the LLM playground is its interactive experience.

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AWS Machine Learning Blog

AUGUST 3, 2023

As machine learning (ML) becomes increasingly prevalent in a wide range of industries, organizations are finding the need to train and serve large numbers of ML models to meet the diverse needs of their customers. The timestamps are randomly generated between the start and end dates specified in the code.

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AWS Machine Learning Blog

FEBRUARY 25, 2025

Instead of relying on predefined, rigid definitions, our approach follows the principle of understanding a set. Its important to note that the learned definitions might differ from common expectations. Instead of relying solely on compressed definitions, we provide the model with a quasi-definition by extension.

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Algorithm Machine Learning Machine Learning K-nearest Neighbors 82

AWS Machine Learning Blog

FEBRUARY 27, 2023

Model tuning is the experimental process of finding the optimal parameters and configurations for a machine learning (ML) model that result in the best possible desired outcome with a validation dataset. Single objective optimization with a performance metric is the most common approach for tuning ML models.

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ML ML AWS Machine Learning 98

AWS Machine Learning Blog

MAY 9, 2023

Amazon SageMaker provides a number of options for users who are looking for a solution to host their machine learning (ML) models. For that use case, SageMaker provides SageMaker single model endpoints (SMEs), which allow you to deploy a single ML model against a logical endpoint. Firstly, we need to define the serving container.

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Python ML ML Deep Learning 109

The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. Supporting the operations of data scientists and ML engineers requires you to reduce—or eliminate—the engineering overhead of building, deploying, and maintaining high-performance models.

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Machine Learning Machine Learning Data Scientist ML 69

How to Learn Machine Learning

MAY 2, 2025

Azure Machine Learning is Microsoft’s enterprise-grade service that provides a comprehensive environment for data scientists and ML engineers to build, train, deploy, and manage machine learning models at scale. You can explore its capabilities through the official Azure ML Studio documentation. Awesome, right?

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Azure Machine Learning Machine Learning Data Science 52

O'Reilly Media

JANUARY 17, 2023

This mindset has followed me into my work in ML/AI. Because if companies use code to automate business rules, they use ML/AI to automate decisions. Given that, what would you say is the job of a data scientist (or ML engineer, or any other such title)? But first, let’s talk about the typical ML workflow.

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ML ML Data Scientist Machine Learning 145

AWS Machine Learning Blog

MAY 8, 2023

SageMaker provides single model endpoints (SMEs), which allow you to deploy a single ML model, or multi-model endpoints (MMEs), which allow you to specify multiple models to host behind a logical endpoint for higher resource utilization. Firstly, we need to define the serving container.

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Dataconomy

APRIL 4, 2025

Definition and role of AI prompt engineers AI prompt engineers are responsible for crafting and refining prompts used in AI models, including OpenAI’s ChatGPT and Google’s Bard. Understanding of AI, ML, and NLP A strong grasp of machine learning concepts, algorithms, and natural language processing is essential in this role.

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Towards AI

SEPTEMBER 10, 2023

I’ve passed many ML courses before, so that I can compare. This one is definitely one of the most practical and inspiring. So you definitely can trust his expertise in Machine Learning and Deep Learning. So you definitely can trust his expertise in Machine Learning and Deep Learning. You start with the working ML model.

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Deep Learning Deep Learning ML ML 124

Towards AI

MARCH 14, 2024

I mean, ML engineers often spend most of their time handling and understanding data. So, how is a data scientist different from an ML engineer? Well, there are three main reasons for this confusing overlap between the role of a data scientist and the role of an ML engineer. Join thousands of data leaders on the AI newsletter.

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Data Science Data Scientist ML ML 104

The MLOps Blog

MAY 31, 2023

And eCommerce companies have a ton of use cases where ML can help. The problem is, with more ML models and systems in production, you need to set up more infrastructure to reliably manage everything. And because of that, many companies decide to centralize this effort in an internal ML platform. But how to build it?

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ML ML Algorithm Machine Learning 59

Towards AI

AUGUST 25, 2023

As a reminder, I highly recommend that you refer to more than one resource (other than documentation) when learning ML, preferably a textbook geared toward your learning level (beginner/intermediate / advanced). In ML, there are a variety of algorithms that can help solve problems.

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AWS Machine Learning Blog

JANUARY 22, 2025

Daniel Pienica is a Data Scientist at Cato Networks with a strong passion for large language models (LLMs) and machine learning (ML). With six years of experience in ML and cybersecurity, he brings a wealth of knowledge to his work. You are only allowed to output text in JSON format.

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AWS Machine Learning Blog

FEBRUARY 6, 2025

A user asking a scientific question aims to translate scientific intent, such as I want to find patients with a diagnosis of diabetes and a subsequent metformin fill, into algorithms that capture these variables in real-world data. An in-context learning technique that includes semantically relevant solved questions and answers in the prompt.

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Pickl AI

JANUARY 29, 2024

Data Science extracts insights, while Machine Learning focuses on self-learning algorithms. Key takeaways Data Science lays the groundwork for Machine Learning, providing curated datasets for ML algorithms to learn and make predictions. Data Science enhances ML accuracy through preprocessing and feature engineering expertise.

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Data Science ML ML Machine Learning 52

AWS Machine Learning Blog

JANUARY 30, 2025

The AML feature store standardizes variable definitions using scientifically validated algorithms. The Smart Subgroups component trains the clustering algorithm and summarizes the most important features of each cluster. The user selects the AML features that define the patient population for analysis.

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Towards AI

AUGUST 16, 2023

In fact, AI/ML graduate textbooks do not provide a clear and consistent description of the AI software engineering process. Therefore, I thought it would be helpful to give a complete description of the AI engineering process or AI Process, which is described in most AI/ML textbooks [5][6]. 85% or more of AI projects fail [1][2].

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The MLOps Blog

MARCH 20, 2023

Long-term ML project involves developing and sustaining applications or systems that leverage machine learning models, algorithms, and techniques. An example of a long-term ML project will be a bank fraud detection system powered by ML models and algorithms for pattern recognition.

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ML ML Machine Learning Machine Learning 59

Towards AI

JANUARY 27, 2025

Definition: What is Machine Learning? Rather than being given step-by-step instructions, ML systems: 🧠 Analyze data,🔍 Identify patterns, and🎯 Make predictions or decisions based on that data. At its core, machine learning teaches computers to make accurate predictions or smart decisions using data.

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Towards AI

AUGUST 6, 2023

Motivation Recent updates in machine learning (ML) and computer vision (CV) are a mouthful, from Stable Diffusion for generative artificial intelligence (AI) to Segment Anything as foundation models. It is getting increasingly difficult to stay up-to-date in the ML or CompVis community. Abhishek Thakur: A new ML algorithm came out?

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