Smart Data Collective

NOVEMBER 1, 2020

Machines, artificial intelligence (AI), and unsupervised learning are reshaping the way businesses vie for a place under the sun. With that being said, let’s have a closer look at how unsupervised machine learning is omnipresent in all industries. What Is Unsupervised Machine Learning?

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Pickl AI

APRIL 13, 2025

Summary: Classifier in Machine Learning involves categorizing data into predefined classes using algorithms like Logistic Regression and Decision Trees. Introduction Machine Learning has revolutionized how we process and analyse data, enabling systems to learn patterns and make predictions.

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Dataconomy

JUNE 29, 2023

Python machine learning packages have emerged as the go-to choice for implementing and working with machine learning algorithms. These libraries, with their rich functionalities and comprehensive toolsets, have become the backbone of data science and machine learning practices.

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Flipboard

NOVEMBER 28, 2024

Addressing this, our study introduces an unsupervised deep learning model, MOSA (Multi-Omic Synthetic Augmentation), specifically designed to integrate and augment the Cancer Dependency Map (DepMap). in the number of multi-omic profiles and thereby generating a complete DepMap for 1523 cancer cell lines.

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How to Learn Machine Learning

MAY 2, 2025

Welcome to this comprehensive guide on Azure Machine Learning , Microsoft’s powerful cloud-based platform that’s revolutionizing how organizations build, deploy, and manage machine learning models. This is where Azure Machine Learning shines by democratizing access to advanced AI capabilities.

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IBM Journey to AI blog

MAY 10, 2024

Underpinning most artificial intelligence (AI) deep learning is a subset of machine learning that uses multi-layered neural networks to simulate the complex decision-making power of the human brain. Deep learning requires a tremendous amount of computing power.

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Dataconomy

APRIL 3, 2025

Researchers, data scientists, and machine learning practitioners alike have embraced t-SNE for its effectiveness in transforming extensive datasets into visual representations, enabling a clearer understanding of relationships, clusters, and patterns within the data.

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Data Science Blog

FEBRUARY 23, 2023

1, Data is the new oil, but labeled data might be closer to it Even though we have been in the 3rd AI boom and machine learning is showing concrete effectiveness at a commercial level, after the first two AI booms we are facing a problem: lack of labeled data or data themselves. Labeled data might be also like uranium.

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The MLOps Blog

NOVEMBER 14, 2023

Deep learning models are typically highly complex. While many traditional machine learning models make do with just a couple of hundreds of parameters, deep learning models have millions or billions of parameters. The reasons for this range from wrongly connected model components to misconfigured optimizers.

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Flipboard

FEBRUARY 13, 2025

This study employs deep learning methods to train interatomic potential parameters for the Fe–O system, achieving precise atomic-scale simulations of the wustite phase structure and internal lattice defects. The study found that cation vacancy defects in wustite tend to aggregate, forming stable cluster structures.

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Flipboard

JANUARY 26, 2025

Here, we present artficial intelligence-based cartography of ensembles (ACE), an end-to-end pipeline that employs three-dimensional deep learning segmentation models and advanced cluster-wise statistical algorithms, to enable unbiased mapping of local neuronal activity and connectivity.

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Flipboard

JUNE 13, 2025

You’ll discover how skills like data handling and machine learning form the backbone of AI innovation, while communication and collaboration ensure your ideas make an impact beyond the technical realm. Key languages include: Python: Known for its simplicity and versatility, Python is the most widely used language in AI.

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IBM Journey to AI blog

JULY 6, 2023

To keep up with the pace of consumer expectations, companies are relying more heavily on machine learning algorithms to make things easier. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other? Machine learning is a subset of AI.

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IBM Journey to AI blog

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. What is machine learning?

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Mlearning.ai

NOVEMBER 3, 2023

And who by gradient descent, who by quick segment Who in the cluster, who in the top percent Who by linear regression, who by SVM Who in random forest, who in deep learning And who shall I say is normalizing? Submission Suggestions Who By Prior: A Machine Learning Song was originally published in MLearning.ai

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Flipboard

JUNE 20, 2023

Machine learning (ML) engineers have traditionally focused on striking a balance between model training and deployment cost vs. performance. For reference, GPT-3, an earlier generation LLM has 175 billion parameters and requires months of non-stop training on a cluster of thousands of accelerated processors.

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Towards AI

APRIL 5, 2023

Rustic Learning: Machine Learning in Rust — Part 2: Regression and Classification An Introduction to Rust’s Machine Learning crates Photo by Malik Skydsgaard on Unsplash Rustic Learning is a series of articles that explores the use of Rust programming language for machine learning tasks.

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Flipboard

FEBRUARY 12, 2025

Here, we present DeepCellMap, a deep-learning-assisted tool that integrates multi-scale image processing with advanced spatial and clustering statistics. This pipeline is designed to map microglial organization during normal and pathological brain development and has the potential to be adapted to any cell type.

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Dataconomy

MARCH 3, 2025

Thanks to machine learning (ML) and artificial intelligence (AI), it is possible to predict cellular responses and extract meaningful insights without the need for exhaustive laboratory experiments. Gene set enrichment : Identify clusters of genes that behave similarly under perturbations and describe their common function.

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Towards AI

APRIL 7, 2024

With the emergence of ARCGISpro which will replace ArcMap by 2026 mainly focusing on data science and machine learning, all the signs that machine learning is the future of GIS and you might have to learn some principles of data science, but where do you start, let us have a look. GIS Random Forest script.

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Google Research AI blog

MARCH 14, 2023

Developing machine learning (ML) tools in pathology to assist with the microscopic review represents a compelling research area with many potential applications. We then used the prognostic model to compute the average ML-predicted risk score for each cluster. This information is central to understanding clinical prognosis (i.e.,

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Analytics Vidhya

SEPTEMBER 16, 2021

This article was published as a part of the Data Science Blogathon Introduction Unsupervised learning, where there are no predefined labels for the data and the model segments the data into groups by inferring patterns and extracting features from the data, is at the heart of the data science problems.

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AWS Machine Learning Blog

NOVEMBER 27, 2024

Mixed Precision Training with FP8 As shown in figure below, FP8 is a datatype supported by NVIDIA’s H100 and H200 GPUs, enables efficient deep learning workloads. More details about FP8 can be found at FP8 Formats For Deep Learning. supports the Llama 3.1 (and Surya Kari is a Senior Generative AI Data Scientist at AWS.

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Pickl AI

FEBRUARY 7, 2025

Summary: Machine Learning and Deep Learning are AI subsets with distinct applications. Introduction In todays world of AI, both Machine Learning (ML) and Deep Learning (DL) are transforming industries, yet many confuse the two. What is Machine Learning? billion by 2030.

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Data Science Dojo

APRIL 27, 2023

Trending GitHub Repositories Scikit-learn:  A Python library for machine learning built on top of NumPy, SciPy, and matplotlib. It provides a range of algorithms for classification, regression, clustering, and more.   PyTorch:  An open-source machine learning library developed by Facebook’s AI research group.

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Data Science Dojo

AUGUST 24, 2023

MLFlow Machine Learning flow MLflow has unique features and characteristics that differentiate it from other MLOps tools, making it appealing to users with specific requirements or preferences: Modularity : One of MLflow’s most significant advantages is its modular architecture.

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Data Science Dojo

AUGUST 30, 2023

Machine learning algorithms Machine learning forms the core of Applied Data Science. It leverages algorithms to parse data, learn from it, and make predictions or decisions without being explicitly programmed. Deep learning Deep learning, a subset of machine learning, has been a game-changer in lots of industries.

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Dataconomy

MAY 22, 2023

But what exactly is distributed learning in machine learning? In this article, we will explore the concept of distributed learning and its significance in the realm of machine learning. Why is it so important? This process is often referred to as training or model optimization.

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Towards AI

MARCH 8, 2025

Figure 1: Gaussian mixture model illustration [Image by AI] Introduction In a time where deep learning (DL) and transformers steal the spotlight, its easy to forget about classic algorithms like K-means, DBSCAN, and GMM. Consider the everyday clustering puzzles: customer segmentation, social network analysis, or image segmentation.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

The compute clusters used in these scenarios are composed of more than thousands of AI accelerators such as GPUs or AWS Trainium and AWS Inferentia , custom machine learning (ML) chips designed by Amazon Web Services (AWS) to accelerate deep learning workloads in the cloud.

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Towards AI

JUNE 27, 2023

Last Updated on June 27, 2023 by Editorial Team Source: Unsplash This piece dives into the top machine learning developer tools being used by developers — start building! In the rapidly expanding field of artificial intelligence (AI), machine learning tools play an instrumental role.

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AWS Machine Learning Blog

MARCH 18, 2025

The rise of generative AI has significantly increased the complexity of building, training, and deploying machine learning (ML) models. It now demands deep expertise, access to vast datasets, and the management of extensive compute clusters.

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AWS Machine Learning Blog

MAY 31, 2023

Running machine learning (ML) workloads with containers is becoming a common practice. With containers, scaling on a cluster becomes much easier. With containers, scaling on a cluster becomes much easier. We use the Neuron SDK to run deep learning workloads on AWS Inferentia and Trainium-based instances.

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ODSC - Open Data Science

MARCH 17, 2023

At the end of the day, why not use an AutoML package (Automated Machine Learning) or an Auto-Forecasting tool and let it do the job for you? However, we already know that: Machine Learning models deliver better results in terms of accuracy when we are dealing with interrelated series and complex patterns in our data.

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Pickl AI

OCTOBER 9, 2023

However, with the emergence of Machine Learning algorithms, the retail industry has seen a revolutionary shift in demand forecasting capabilities. This technology allows computers to learn from historical data, identify patterns, and make data-driven decisions without explicit programming.

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Flipboard

MAY 5, 2025

Amazon OpenSearch Service is a fully managed solution that simplifies the deployment, operation, and scaling of OpenSearch clusters in the AWS Cloud. Figure 2 : Amazon OpenSearch Service for Vector Search: Demo Key Features of AWS OpenSearch Scalability: Easily scale clusters up or down based on workload demands.

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Pickl AI

AUGUST 3, 2023

Introduction to Deep Learning Algorithms: Deep learning algorithms are a subset of machine learning techniques that are designed to automatically learn and represent data in multiple layers of abstraction. How Deep Learning Algorithms Work?

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