ODSC - Open Data Science

FEBRUARY 17, 2023

Natural language processing (NLP) has been growing in awareness over the last few years, and with the popularity of ChatGPT and GPT-3 in 2022, NLP is now on the top of peoples’ minds when it comes to AI. Developing NLP tools isn’t so straightforward, and requires a lot of background knowledge in machine & deep learning, among others.

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Google Research AI blog

FEBRUARY 28, 2023

In 2022, we expanded our research interactions and programs to faculty and students across Latin America , which included grants to women in computer science in Ecuador. We also help make global conferences accessible to more researchers around the world, for example, by funding 24 students this year to attend Deep Learning Indaba in Tunisia.

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Pickl AI

DECEMBER 26, 2024

billion by the end of 2024 , reflecting a remarkable increase from $29 billion in 2022. The primary components include: Graphics Processing Units (GPUs) These are specially designed for parallel processing, making them ideal for training deep learning models. The global Generative AI market is projected to exceed $66.62

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NYU Center for Data Science

JANUARY 27, 2023

The research explores machine learning methods in image coaddition, a process used by astronomers to combine multiple images into a single higher-resolution image. CDS spoke with Harlan about the project, deep learning methods in the field of astronomy, and advice for current CDS students.

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Hacker News

JUNE 27, 2023

For example, on a commercially available cluster of 3,584 H100 GPUs co-developed by startup Inflection AI and operated by CoreWeave , a cloud service provider specializing in GPU-accelerated workloads, the system completed the massive GPT-3-based training benchmark in less than eleven minutes.

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IBM Journey to AI blog

DECEMBER 20, 2023

Unsupervised machine learning Unsupervised learning algorithms—like Apriori, Gaussian Mixture Models (GMMs) and principal component analysis (PCA)—draw inferences from unlabeled datasets, facilitating exploratory data analysis and enabling pattern recognition and predictive modeling.

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AWS Machine Learning Blog

SEPTEMBER 12, 2024

LLMs disrupt the industry Towards the end of 2022, groundbreaking LLMs were released that realized drastic improvements over previous model capabilities. In order to provision a highly scalable cluster that is resilient to hardware failures, Thomson Reuters turned to Amazon SageMaker HyperPod. Chinchilla point 52b 132b 260b 600b 1.3t

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Flipboard

JULY 10, 2023

Figure 5: Architecture of Convolutional Autoencoder for Image Segmentation (source: Bandyopadhyay, “Autoencoders in Deep Learning: Tutorial & Use Cases [2023],” V7Labs , 2023 ). VAEs can generate new samples from the learned latent distribution, making them ideal for image generation and style transfer tasks.

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AWS Machine Learning Blog

MARCH 6, 2023

These factors require training an LLM over large clusters of accelerated machine learning (ML) instances. Within one launch command, Amazon SageMaker launches a fully functional, ephemeral compute cluster running the task of your choice, and with enhanced ML features such as metastore, managed I/O, and distribution.

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AWS Machine Learning Blog

JUNE 7, 2023

For example, GPT-3 (2020) and BLOOM (2022) feature around 175 billion parameters, Gopher (2021) has 230 billion parameters, and MT-NLG (2021) 530 billion parameters. In 2022, Hoffman et al. In 2022, Hoffman et al. They implemented their guidance in the 70B parameter Chinchilla (2022) model, that outperformed much bigger models.

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AWS Machine Learning Blog

APRIL 19, 2023

The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. With the DJL, integrating this deep learning is simple. Business requirements We are the US squad of the Sportradar AI department. The architecture of DJL is engine agnostic.

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PyImageSearch

JUNE 19, 2023

machine learning, statistics, probability, and algebra) are employed to recommend our popular daily applications. Each service uses unique techniques and algorithms to analyze user data and provide recommendations that keep us returning for more. Figure 1: Distribution of applications of recommendation systems (source: Ko et al.,

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Mlearning.ai

JULY 6, 2023

NOTES, DEEP LEARNING, REMOTE SENSING, ADVANCED METHODS, SELF-SUPERVISED LEARNING A note of the paper I have read Photo by Kelly Sikkema on Unsplash Hi everyone, In today’s story, I would share notes I took from 32 pages of Wang et al., 2022’s paper. 2022 Deep learning notoriously needs a lot of data in training.

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Applied Data Science

AUGUST 2, 2021

Big Ideas What to look out for in 2022 1. They bring deep expertise in machine learning , clustering , natural language processing , time series modelling , optimisation , hypothesis testing and deep learning to the team. Automation Automating data pipelines and models ➡️ 6.

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AWS Machine Learning Blog

APRIL 12, 2023

They’ve built a deep-learning model ScarceGAN, which focuses on identification of extremely rare or scarce samples from multi-dimensional longitudinal telemetry data with small and weak labels. This work has been published in CIKM’21 and is open source for rare class identification for any longitudinal telemetry data.

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AWS Machine Learning Blog

JANUARY 30, 2023

In these cases, you might be able to speed up the process by distributing training over multiple machines or processes in a cluster. This post discusses how SageMaker LightGBM helps you set up and launch distributed training, without the expense and difficulty of directly managing your training clusters. 1 5329 5414 0.937 0.947 65.6

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Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Tools and frameworks like Scikit-Learn, TensorFlow, and Keras are often covered.

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The MLOps Blog

MARCH 23, 2023

Large model sizes The MT-NLG model released in 2022 has 530 billion parameters and requires several hundred gigabytes of storage. Even for basic inference on LLM, multiple accelerators or multi-node computing clusters like multiple Kubernetes pods are required. 2022 where they show how to train a model on a fixed-compute budget.

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ODSC - Open Data Science

OCTOBER 18, 2023

billion in 2022, an increase of 21.3% 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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Snorkel AI

APRIL 11, 2023

Cody Coleman, CEO and co-founder of Coactive AI gave a presentation entitled “Data Selection for Data-Centric AI: Quality over Quantity” at Snorkel AI’s Future of Data-Centric AI Event in August 2022. Active learning is a really powerful data selection technique for reducing labeling costs. And this work appeared in AAAI 2022.

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Snorkel AI

APRIL 11, 2023

Cody Coleman, CEO and co-founder of Coactive AI gave a presentation entitled “Data Selection for Data-Centric AI: Quality over Quantity” at Snorkel AI’s Future of Data-Centric AI Event in August 2022. Active learning is a really powerful data selection technique for reducing labeling costs. And this work appeared in AAAI 2022.

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Snorkel AI

APRIL 11, 2023

Cody Coleman, CEO and co-founder of Coactive AI gave a presentation entitled “Data Selection for Data-Centric AI: Quality over Quantity” at Snorkel AI’s Future of Data-Centric AI Event in August 2022. Active learning is a really powerful data selection technique for reducing labeling costs. And this work appeared in AAAI 2022.

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Pickl AI

NOVEMBER 28, 2024

A Machine Learning Engineer is crucial in designing, building, and deploying models that drive this transformation. The global Machine Learning market was valued at USD 35.80 billion in 2022 and is expected to grow to USD 505.42 Neural networks are the foundation of Deep Learning techniques.

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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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How to Learn Machine Learning

AUGUST 14, 2024

scikit-learn – The most widely Machine learning for text used for Python, scikit-learn is an open-source, free machine learning library. It has many useful tools for stats modeling and machine learning including regression, classification, and clustering.

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Defined.ai blog

OCTOBER 9, 2022

The average cost of a data breach was $4.35M in 2022 , and it took an average of 277 days for a company to identify and contain a breach. from 2022 to 2030. Clustering saves serious time in data analysis by grouping together similar and/or related data, revealing when there are patterns of unique activity and behavior.

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Pickl AI

SEPTEMBER 3, 2023

Further, it will provide a step-by-step guide on anomaly detection Machine Learning python. Key Takeaways: As of 2021, the market size of Machine Learning was USD 25.58 CAGR during 2022-2030. By 2028, the market value of global Machine Learning is projected to be $31.36 Billion which is supposed to increase by 35.6%

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Google Research AI blog

NOVEMBER 16, 2022

Posted by Yanqi Zhou, Research Scientist, Google Research, Brain Team The capacity of a neural network to absorb information is limited by the number of its parameters, and as a consequence, finding more effective ways to increase model parameters has become a trend in deep learning research.

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PyImageSearch

JULY 3, 2023

These features can be simple metadata or model-based features (extracted from a deep learning model), representing how good that video is for a member. Users are grouped into small clusters based on their viewing history to obtain context-only features. Context features refer to the user and his query (e.g., That’s not the case.

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AWS Machine Learning Blog

APRIL 18, 2023

One of the major challenges in training and deploying LLMs with billions of parameters is their size, which can make it difficult to fit them into single GPUs, the hardware commonly used for deep learning. We select Amazon’s SEC filing reports for years 2021–2022 as the training data to fine-tune the GPT-J 6B model.

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Heartbeat

NOVEMBER 17, 2023

clustering, matching) can dictate the best metric. from langchain.evaluation import RegexMatchStringEvaluator evaluator = RegexMatchStringEvaluator() evaluator.evaluate_strings( prediction="The date is 2022-01-01", reference="The date is 2022-01-01" ) # {'score': 1} # Check for the presence of a MM-DD-YYYY string.

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AWS Machine Learning Blog

JANUARY 13, 2023

FedML supports several out-of-the-box deep learning algorithms for various data types, such as tabular, text, image, graphs, and Internet of Things (IoT) data. Please review the presentation at re:MARS 2022 focused on “ Managed Federated Learning on AWS: A case study for healthcare ” for a detailed walkthrough of this solution.

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Mlearning.ai

JUNE 8, 2023

Clustering  — we can cluster our sentences, useful for topic modeling. text-similarity-{ada, babbage, curie, davinci}-001, use cases: Clustering, regression, anomaly detection, visualization Text search : Semantic information retrieval over documents. The article is clustering “Fine Food Reviews” dataset. lower price.

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Pickl AI

NOVEMBER 18, 2024

Introduction Machine Learning is critical in shaping modern technologies, from autonomous vehicles to personalised recommendations. The global Machine Learning market was valued at USD 35.80 billion in 2022 and is expected to grow significantly, reaching USD 505.42 For unSupervised Learning tasks (e.g., Random Forests).

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Pickl AI

DECEMBER 10, 2024

Introduction Machine Learning has become a cornerstone in transforming industries worldwide. billion in 2022 and is projected to grow at a CAGR of 34.8% A key aspect of building effective Machine Learning models is feature extraction in Machine Learning. The global market was valued at USD 36.73 from 2023 to 2030.

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Google Research AI blog

JULY 26, 2023

Posted by Eleni Triantafillou, Research Scientist, and Malik Boudiaf, Student Researcher, Google Deep learning has recently made tremendous progress in a wide range of problems and applications, but models often fail unpredictably when deployed in unseen domains or distributions.

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AssemblyAI

MARCH 7, 2023

Data requirements for GPT-3 and a 100T parameter model according to OpenAI's 2020 and DeepMind's 2022 scaling laws. This is about 20 times more data than expected based on the scaling laws in [ 3 ], and a staggering 4,000 times more data than GPT-3.

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