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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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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insideBIGDATA

MARCH 4, 2024

Hammerspace, the company orchestrating the Next Data Cycle, unveiled the high-performance NAS architecture needed to address the requirements of broad-based enterprise AI, machine learning and deep learning (AI/ML/DL) initiatives and the widespread rise of GPU computing both on-premises and in the cloud.

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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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Clustering Deep Learning Deep Learning Computer Science 94

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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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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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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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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Clustering Exploratory Data Analysis Data Analysis Data Analysis 91

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.

Deep Learning 102

Deep Learning Deep Learning Clustering Computer Science 102

Data Science Dojo

JULY 15, 2024

Recurrent Neural Networks (RNNs): These powerful deep learning models can learn complex patterns and long-term dependencies within time series data, making them suitable for more intricate forecasting tasks. Clustering Algorithms: Clustering algorithms can group data points with similar features. shirt, pants).

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Google Research AI blog

MARCH 14, 2023

To our knowledge, this is the first demonstration that medical experts can learn new prognostic features from machine learning, a promising start for the future of this “learning from deep learning” paradigm. We then used the prognostic model to compute the average ML-predicted risk score for each cluster.

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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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AWS Machine Learning Blog

OCTOBER 16, 2024

Although setting up a processing cluster is an alternative, it introduces its own set of complexities, from data distribution to infrastructure management. We use the purpose-built geospatial container with SageMaker Processing jobs for a simplified, managed experience to create and run a cluster. format("/".join(tile_prefix),

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ML ML Clustering Machine Learning 115

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 Outside of work, he enjoys running, hiking, and cooking.

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Dataconomy

FEBRUARY 28, 2023

Deep learning models have emerged as a powerful tool in the field of ML, enabling computers to learn from vast amounts of data and make decisions based on that learning. In this article, we will explore the importance of deep learning models and their applications in various fields.

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Pickl AI

APRIL 9, 2025

Summary: Artificial Intelligence (AI) and Deep Learning (DL) are often confused. AI vs Deep Learning is a common topic of discussion, as AI encompasses broader intelligent systems, while DL is a subset focused on neural networks. Is Deep Learning just another name for AI? Is all AI Deep Learning?

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Dataconomy

MARCH 3, 2025

Gene set enrichment : Identify clusters of genes that behave similarly under perturbations and describe their common function. Single-cell ML models (SCGPT) : These use deep learning to predict gene expression levels but struggle to provide clear biological explanations.

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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.

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IBM Journey to AI blog

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other?

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Deep Learning Deep Learning Machine Learning Machine Learning 71

Flipboard

FEBRUARY 16, 2023

Modern model pre-training often calls for larger cluster deployment to reduce time and cost. In October 2022, we launched Amazon EC2 Trn1 Instances , powered by AWS Trainium , which is the second generation machine learning accelerator designed by AWS. We use Slurm as the cluster management and job scheduling system.

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Hacker News

OCTOBER 15, 2024

Over the course of 2023, we rapidly scaled up our training clusters from 1K, 2K, 4K, to eventually 16K GPUs to support our AI workloads. Today, we’re training our models on two 24K-GPU clusters. We don’t expect this upward trajectory for AI clusters to slow down any time soon. Building AI clusters requires more than just GPUs.

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Clustering AI AI Deep Learning 175

PyImageSearch

SEPTEMBER 16, 2024

Home Table of Contents Credit Card Fraud Detection Using Spectral Clustering Understanding Anomaly Detection: Concepts, Types and Algorithms What Is Anomaly Detection? Spectral clustering, a technique rooted in graph theory, offers a unique way to detect anomalies by transforming data into a graph and analyzing its spectral properties.

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Clustering Algorithm Machine Learning Machine Learning 57

Dataconomy

FEBRUARY 20, 2025

This is the goal behind Neurosymbolic AI , a new approach that merges deep learning with coherence-driven inference (CDI). To maximize coherence by separating true and false statements into different clusters. If a proposition supports another , it gets a positive connection.

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Smart Data Collective

NOVEMBER 1, 2020

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? If you’ve ever come across deep learning, you might have heard about two methods to teach machines: supervised and unsupervised. Source ].

Machine Learning 145

Machine Learning Machine Learning Clustering Data Mining 145

Data Science Dojo

APRIL 27, 2023

It provides a range of algorithms for classification, regression, clustering, and more.   Link to the repository:  [link]   TensorFlow:  An open-source machine learning library developed by Google Brain Team. TensorFlow is used for numerical computation using data flow graphs.  

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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. Clustering and anomaly detection are examples of unsupervised learning tasks.

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Dataconomy

MARCH 4, 2025

Clustering Clustering groups similar data points based on their attributes. One common example is k-means clustering, which segments data into distinct groups for analysis. They’re pivotal in deep learning and are widely applied in image and speech recognition.

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Flipboard

JUNE 20, 2023

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. The Carbontracker study estimates that training GPT-3 from scratch may emit up to 85 metric tons of CO2 equivalent, using clusters of specialized hardware accelerators.

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AWS Machine Learning Blog

APRIL 19, 2024

Our deep learning models have non-trivial requirements: they are gigabytes in size, are numerous and heterogeneous, and require GPUs for fast inference and fine-tuning. The architecture deploys a simple service in a Kubernetes pod within an EKS cluster. xlarge nodes is included to run system pods that are needed by the cluster.

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Data Science Dojo

AUGUST 30, 2023

It leverages algorithms to parse data, learn from it, and make predictions or decisions without being explicitly programmed. From decision trees and neural networks to regression models and clustering algorithms, a variety of techniques come under the umbrella of machine learning.

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Smart Data Collective

JUNE 4, 2021

Clustering (Unsupervised). With Clustering the data is divided into groups. By applying clustering based on distance, the villages are divided into groups. The center of each cluster is the optimal location for setting up health centers. The center of each cluster is the optimal location for setting up health centers.

Data Science 133

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AWS Machine Learning Blog

APRIL 1, 2024

Distributed model training requires a cluster of worker nodes that can scale. In this blog post, AWS collaborates with Meta’s PyTorch team to discuss how to use the PyTorch FSDP library to achieve linear scaling of deep learning models on AWS seamlessly using Amazon EKS and AWS Deep Learning Containers (DLCs).

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AWS Machine Learning Blog

DECEMBER 22, 2023

As a result, machine learning practitioners must spend weeks of preparation to scale their LLM workloads to large clusters of GPUs. Integrating tensor parallelism to enable training on massive clusters This release of SMP also expands PyTorch FSDP’s capabilities to include tensor parallelism techniques.

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AWS Machine Learning Blog

FEBRUARY 1, 2023

Recent developments in deep learning have led to increasingly large models such as GPT-3, BLOOM, and OPT, some of which are already in excess of 100 billion parameters. Many enterprise customers choose to deploy their deep learning workloads using Kubernetes—the de facto standard for container orchestration in the cloud.

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Dataconomy

JUNE 29, 2023

These packages are built to handle various aspects of machine learning, including tasks such as classification, regression, clustering, dimensionality reduction, and more. In addition to machine learning-specific packages, there are also general-purpose scientific computing libraries that are commonly used in machine learning projects.

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AssemblyAI

DECEMBER 4, 2024

This process relies on advanced algorithms and deep learning models to differentiate between voices, producing a structured transcript with clear speaker boundaries. Speaker Embeddings with Deep Learning models : Once the audio is segmented, each segment is processed using a deep learning model to extract speaker embeddings.

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AWS Machine Learning Blog

JUNE 11, 2024

release , you can now launch Neuron DLAMIs (AWS Deep Learning AMIs) and Neuron DLCs (AWS Deep Learning Containers) with the latest released Neuron packages on the same day as the Neuron SDK release. AWS DLCs provide a set of Docker images that are pre-installed with deep learning frameworks.

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