ODSC - Open Data Science

MARCH 27, 2023

Botnets Detection at Scale — Lesson Learned from Clustering Billions of Web Attacks into Botnets. You will use the same example to explore both approaches utilizing TensorFlow in a Colab notebook.

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Google Research AI blog

APRIL 30, 2023

Posted by Catherine Armato, Program Manager, Google The Eleventh International Conference on Learning Representations (ICLR 2023) is being held this week as a hybrid event in Kigali, Rwanda. We are proud to be a Diamond Sponsor of ICLR 2023, a premier conference on deep learning, where Google researchers contribute at all levels.

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NYU Center for Data Science

JANUARY 25, 2024

2023’s event, held in New Orleans in December, was no exception, showcasing groundbreaking research from around the globe. In the world of data science, few events garner as much attention and excitement as the annual Neural Information Processing Systems (NeurIPS) conference.

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Mlearning.ai

APRIL 10, 2023

How this machine learning model has become a sustainable and reliable solution for edge devices in an industrial network An Introduction Clustering (cluster analysis - CA) and classification are two important tasks that occur in our daily lives. Industrial Internet of Things (IIoT) The Constraints Within the area of Industry 4.0,

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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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Data Science Dojo

JANUARY 31, 2024

Facebook AI similarity search (FAISS) FAISS is used for similarity search and clustering dense vectors. PyTorch and TensorFlow These are commonly used deep learning frameworks that offer immense flexibility in building RAG models. IBM used this mechanism during the US Open 2023 for live commentary.

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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. 32xlarge instance type, launching in early 2023, will increase this bandwidth to 1600 Gbps per instance. trn1.32xlarge 16 512 128 512 800 trn1n.32xlarge

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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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NYU Center for Data Science

NOVEMBER 15, 2023

We’re excited to announce that many CDS faculty, researchers, and students will present at the upcoming thirty-seventh 2023 NeurIPS (Neural Information Processing Systems) Conference , taking place Sunday, December 10 through Saturday, December 16. The conference will take place in-person at the New Orleans Ernest N.

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Flipboard

JULY 10, 2023

Figure 3: Latent space visualization of the closet (source: Kumar, “Autoencoder vs Variational Autoencoder (VAE): Differences,” Data Analytics , 2023 ). Figure 5: Architecture of Convolutional Autoencoder for Image Segmentation (source: Bandyopadhyay, “Autoencoders in Deep Learning: Tutorial & Use Cases [2023],” V7Labs , 2023 ).

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Pickl AI

DECEMBER 26, 2024

The primary components include: Graphics Processing Units (GPUs) These are specially designed for parallel processing, making them ideal for training deep learning models. Foundation Models Foundation models are pre-trained deep learning models that serve as the backbone for various generative applications.

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Data Science Dojo

JULY 2, 2024

Adaptive AI has risen as a transformational technological concept over the years, leading Gartner to name it as a top strategic tech trend for 2023. Unsupervised Learning : The system learns patterns and structures in unlabeled data, often identifying hidden relationships or clustering similar data points.

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ODSC - Open Data Science

JULY 22, 2023

We’re a few weeks removed from ODSC Europe 2023 and we couldn’t have left on a better note. Here are some highlights from ODSC Europe 2023, including some pictures of speakers and attendees, popular talks, and a summary of what kept people busy. That’s it for our ODSC Europe 2023 highlights! What’s next?

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AssemblyAI

JUNE 24, 2024

Today, many modern Speech-to-Text APIs and Speaker Diarization libraries apply advanced Deep Learning models to perform tasks (A) and (B) near human-level accuracy, significantly increasing the utility of Speaker Diarization APIs. An embedding is a Deep Learning model’s low-dimensional representation of an input.

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AWS Machine Learning Blog

SEPTEMBER 26, 2024

However, building large distributed training clusters is a complex and time-intensive process that requires in-depth expertise. Amazon SageMaker HyperPod, introduced during re:Invent 2023, is a purpose-built infrastructure designed to address the challenges of large-scale training.

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Towards AI

APRIL 5, 2023

Last Updated on April 6, 2023 by Editorial Team Author(s): Ulrik Thyge Pedersen Originally published on Towards AI. The articles cover a range of topics, from the basics of Rust to more advanced machine learning concepts, and provide practical examples to help readers get started with implementing ML algorithms in Rust.

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ODSC - Open Data Science

JULY 14, 2023

Below you’ll find just a few of the many expert-led sessions at ODSC Europe 2023 that attendees loved — and you can view them for yourself here ! And don’t miss the chance to join us for our upcoming free virtual Generative AI Summit on July 20th and ODSC West 2023 in San Francisco (October 31st-November 3rd). What’s next?

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ODSC - Open Data Science

JUNE 7, 2023

Gözde Gül Şahin | Assistant Professor, KUIS AI Fellow | KOC University Fraud Detection with Machine Learning: Laura Mitchell | Senior Data Science Manager | MoonPay Deep Learning and Comparisons between Large Language Models: Hossam Amer, PhD | Applied Scientist | Microsoft Multimodal Video Representations and Their Extension to Visual Language Navigation: (..)

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Becoming Human

MAY 15, 2023

Unsupervised Learning In this type of learning, the algorithm is trained on an unlabeled dataset, where no correct output is provided. Performance Metrics These are used to evaluate the performance of a machine-learning algorithm. Some popular libraries used for deep learning are Keras , PyTorch , and TensorFlow.

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Pickl AI

JANUARY 23, 2023

It is mainly used for deep learning applications. PyTorch PyTorch is a popular, open-source, and lightweight machine learning and deep learning framework built on the Lua-based scientific computing framework for machine learning and deep learning algorithms.

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ODSC - Open Data Science

OCTOBER 18, 2023

Nevertheless, we are still left with the question: How can we do machine learning better? To find out, we’ve taken some of the upcoming tutorials and workshops from ODSC West 2023 and let the experts via their topics guide us toward building better machine learning.

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PyImageSearch

JANUARY 9, 2023

To learn how to develop Face Recognition applications using Siamese Networks, just keep reading. Jump Right To The Downloads Section Face Recognition with Siamese Networks, Keras, and TensorFlow Deep learning models tend to develop a bias toward the data distribution on which they have been trained. That’s not the case.

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ODSC - Open Data Science

AUGUST 15, 2023

You’ll get hands-on practice with unsupervised learning techniques, such as K-Means clustering, and classification algorithms like decision trees and random forest. Finally, you’ll explore how to handle missing values and training and validating your models using PySpark.

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Towards AI

JULY 21, 2023

Last Updated on July 21, 2023 by Editorial Team Author(s): Ricky Costa Originally published on Towards AI. To further comment on Fury, for those looking to intern in the short term, we have a position available to work in an NLP deep learning project in the healthcare domain. Fury What a week. Let’s recap.

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Towards AI

APRIL 14, 2023

Last Updated on April 17, 2023 by Editorial Team Author(s): Kevin Berlemont, PhD Originally published on Towards AI. The feature space reduction is performed by aggregating clusters of features of balanced size. This clustering is usually performed using hierarchical clustering.

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Towards AI

JULY 19, 2023

Last Updated on July 19, 2023 by Editorial Team Author(s): Yashashri Shiral Originally published on Towards AI. This is part 2, and you will learn how to do sales prediction using Time Series.

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NYU Center for Data Science

SEPTEMBER 13, 2024

The world of multi-view self-supervised learning (SSL) can be loosely grouped into four families of methods: contrastive learning, clustering, distillation/momentum, and redundancy reduction. This behavior appears to contradict the classical bias-variance tradeoff, which traditionally suggests a U-shaped error curve.

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PyImageSearch

JUNE 19, 2023

Clustering Clustering is a class of algorithms that segregates the data into a set of definite clusters such that similar points lie in the same cluster and dissimilar points lie in different clusters. Several clustering algorithms (e.g., means and spectral clustering) can be used in recommendation engines.

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Mlearning.ai

JUNE 6, 2023

In today’s blog, we will see some very interesting Python Machine Learning projects with source code. This list will consist of Machine learning projects, Deep Learning Projects, Computer Vision Projects , and all other types of interesting projects with source codes also provided. This is a simple project.

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Towards AI

MAY 3, 2023

Last Updated on May 9, 2023 by Editorial Team Author(s): Sriram Parthasarathy Originally published on Towards AI. This code can cover a diverse array of tasks, such as creating a KMeans cluster, in which users input their data and ask ChatGPT to generate the relevant code.

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PyImageSearch

OCTOBER 2, 2023

Jump Right To The Downloads Section A Deep Dive into Variational Autoencoder with PyTorch Introduction Deep learning has achieved remarkable success in supervised tasks, especially in image recognition. Similar class labels tend to form clusters, as observed with the Convolutional Autoencoder. That’s not the case.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

Learning means identifying and capturing historical patterns from the data, and inference means mapping a current value to the historical pattern. The following figure illustrates the idea of a large cluster of GPUs being used for learning, followed by a smaller number for inference.

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Mlearning.ai

JANUARY 17, 2024

Traditional AI can recognize, classify, and cluster, but not generate the data it is trained on. al 600+: Key technological concepts of generative AI 300+: Deep Learning — the core of any generative AI model: Deep learning is a central concept of traditional AI that has been adopted and further developed in generative AI.

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Pickl AI

JULY 20, 2023

Top 15 Data Analytics Projects in 2023 for Beginners to Experienced Levels: Data Analytics Projects allow aspirants in the field to display their proficiency to employers and acquire job roles. Image Recognition with Deep Learning: Use Python with TensorFlow or PyTorch to build an image recognition model (e.g., ImageNet).

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PyImageSearch

APRIL 3, 2023

Jump Right To The Downloads Section Deploying a Custom Image Classifier on an OAK-D Introduction As a deep learning engineer or practitioner, you may be working in a team building a product that requires you to train deep learning models on a specific data modality (e.g., computer vision) on a daily basis.

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DrivenData Labs

FEBRUARY 3, 2025

I love participating in various competitions involving deep learning, especially tasks involving natural language processing or LLMs. Issac Chan is a Machine Learning Engineer at Verto where he leverages advanced machine learning techniques to create impactful healthcare solutions. Alejandro A.

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The MLOps Blog

MARCH 23, 2023

Even for basic inference on LLM, multiple accelerators or multi-node computing clusters like multiple Kubernetes pods are required. But the issue we found was that MP is efficient in single-node clusters, but in a multi-node setting, the inference isn’t efficient. For instance, a 1.5B This is because of the low bandwidth networks.

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