AWS Machine Learning Blog

APRIL 7, 2025

This approach allows for greater flexibility and integration with existing AI and machine learning (AI/ML) workflows and pipelines. yml file from the AWS Deep Learning Containers GitHub repository, illustrating how the model synthesizes information across an entire repository. billion to a projected $574.78

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NYU Center for Data Science

JULY 18, 2023

Read about the research groups at CDS working to advance data science and machine learning! CDS includes a range of research groups that bring together NYU professors, faculty fellows, and PhD students working at various intersections of data science, machine learning, and artificial intelligence.

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Towards AI

SEPTEMBER 1, 2023

SpaCy is a language processing library written in Python and Cython that has been well-established since 2016. The majority of processing is a combination of deep learning, Transformers technologies (since version 3.0), and statistical analysis.

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Dataconomy

APRIL 18, 2023

Understanding the basics of artificial intelligence Artificial intelligence is an interdisciplinary field of study that involves creating intelligent machines that can perform tasks that typically require human-like cognitive abilities such as learning, reasoning, and problem-solving.

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Heartbeat

SEPTEMBER 13, 2023

This guide will buttress explainability in machine learning and AI systems. The explainability concept involves providing insights into the decisions and predictions made by artificial intelligence (AI) systems and machine learning models. What is Explainability?

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Explosion

NOVEMBER 9, 2016

now features deep learning models for named entity recognition, dependency parsing, text classification and similarity prediction based on the architectures described in this post. You can now also create training and evaluation data for these models with Prodigy , our new active learning-powered annotation tool. Bowman et al.

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DrivenData Labs

MARCH 30, 2023

Privacy-enhancing technologies (PETs) have the potential to unlock more trustworthy innovation in data analysis and machine learning. Federated learning is one such technology that enables organizations to analyze sensitive data while providing improved privacy protections. What motivated you to participate?

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Mlearning.ai

JANUARY 27, 2023

Save this blog for comprehensive resources for computer vision Source: appen Working in computer vision and deep learning is fantastic because, after every few months, someone comes up with something crazy that completely changes your perspective on what is feasible. Template Matching —  Video Tutorial , Written Tutorial 12.

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Dataconomy

FEBRUARY 22, 2023

The recent victory of a human player over a Go-playing AI system highlights a crucial issue in the field of machine learning prediction: the vulnerability of these systems to adversarial attacks. As machine learning prediction becomes increasingly pervasive, the impact of these limitations will only become more significant.

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AWS Machine Learning Blog

JANUARY 17, 2024

SageMaker Studio is an integrated development environment (IDE) that provides a single web-based visual interface where you can access purpose-built tools to perform all machine learning (ML) development steps, from preparing data to building, training, and deploying your ML models. He retired from EPFL in December 2016.nnIn

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PyImageSearch

NOVEMBER 13, 2023

Home Table of Contents Faster R-CNNs Object Detection and Deep Learning Measuring Object Detector Performance From Where Do the Ground-Truth Examples Come? One of the most popular deep learning-based object detection algorithms is the family of R-CNN algorithms, originally introduced by Girshick et al.

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Towards AI

FEBRUARY 27, 2024

The whole machine learning industry since the early days was growing on open source solutions like scikit learn (2007) and then deep learning frameworks — TensorFlow (2015) and PyTorch (2016). More than 99% of Fortune 500 companies use open-source code [2].

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Dataconomy

JUNE 15, 2023

This demonstrated the astounding potential of machines to learn and differentiate between various objects. In 2016, Google’s AI AlphaGo defeated Lee Sedol and Fan Hui, the European and world champions in the game of Go. Deep learning emerged as a highly promising machine learning technology for various applications.

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Pickl AI

AUGUST 22, 2024

Introduction Deep Learning frameworks are crucial in developing sophisticated AI models, and driving industry innovations. By understanding their unique features and capabilities, you’ll make informed decisions for your Deep Learning applications. Below is an overview of the pros and cons of using PyTorch.

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ODSC - Open Data Science

OCTOBER 4, 2024

Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup (acquired), and taught Machine Learning Systems Design at Stanford. Previously, Ali was the Head of Machine Learning & Worldwide TechLeader for AWS AI / ML specialist solution architects. Sign me up!

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AWS Machine Learning Blog

MARCH 29, 2023

there is enormous potential to use machine learning (ML) for quality prediction. Accelerate time to results and optimize operations by modernizing your business approach from edge to cloud using Machine Learning on AWS. Since 2016 he mentored hundreds of entrepreneurs at startup incubation programs pro-bono.

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AWS Machine Learning Blog

OCTOBER 10, 2023

Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. Federated learning (FL) is a distributed ML approach that trains ML models on distributed datasets. Her current areas of interest include federated learning, distributed training, and generative AI.

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Towards AI

JULY 19, 2023

Deep learning algorithms can be applied to solving many challenging problems in image classification. Therefore, Now we conquer this problem of detecting the cracks using image processing methods, deep learning algorithms, and Computer Vision. 1030–1033, 2016. View at: Publisher Site | Google Scholar R.

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AWS Machine Learning Blog

MAY 10, 2023

Project Jupyter is a multi-stakeholder, open-source project that builds applications, open standards, and tools for data science, machine learning (ML), and computational science. In 2016, he co-created the Altair package for statistical visualization in Python.

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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. The Companys net income attributable to the Company for the year ended December 31, 2016 was $4,816,000, or $0.28

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Mlearning.ai

MARCH 9, 2023

Automated algorithms for image segmentation have been developed based on various techniques, including clustering, thresholding, and machine learning (Arbeláez et al., Background The Markov Blanket Discovery (MBD) approach is a graphical model-based method used for feature selection and causal discovery in machine learning (Peng et al.,

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ODSC - Open Data Science

SEPTEMBER 22, 2023

Gaurav Rao | EVP & GM Machine Learning and AI | AtScale Gaurav is currently the Executive Vice President and General Manager of Machine Learning and AI at AtScale. Most recently, Gaurav served as VP of Product at Neural Magic — innovators in software acceleration for deep learning utilizing sparse model architectures.

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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. 3 feature visual representation of a K-means Algorithm.

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AWS Machine Learning Blog

JULY 13, 2023

Recent years have shown amazing growth in deep learning neural networks (DNNs). International Conference on Machine Learning. On large-batch training for deep learning: Generalization gap and sharp minima.” arXiv preprint arXiv:1609.04836 (2016). [3] PMLR, 2018. [2] 2] Keskar, Nitish Shirish, et al.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). Together, these elements lead to the start of a period of dramatic progress in ML, with NN being redubbed deep learning. Thirdly, the presence of GPUs enabled the labeled data to be processed.

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ML Review

MAY 3, 2018

The addition of language may eventually provide a means for machines to group, reason and articulate complex concepts in the future. Much the same way we iterate, link and update concepts through whatever modality of input our brain takes — multi-modal approaches in deep learning are coming to the fore.

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Explosion

JANUARY 12, 2018

We founded Explosion in October 2016, so this was our first full calendar year in operation. Highlights included: Developed new deep learning models for text classification, parsing, tagging, and NER with near state-of-the-art accuracy. spaCy’s Machine Learning library for NLP in Python. Here’s what we got done.

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Pickl AI

JULY 24, 2024

Key milestones include the Turing Test, the Dartmouth Conference, and breakthroughs in machine learning. Researchers began to focus on Machine Learning, a subfield of AI that emphasises the importance of data-driven approaches. This shift allowed systems to learn from experience and improve their performance over time.

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ML Review

FEBRUARY 26, 2018

Tasks such as “I’d like to book a one-way flight from New York to Paris for tomorrow” can be solved by the intention commitment + slot filing matching or deep reinforcement learning (DRL) model. Chitchatting, such as “I’m in a bad mood”, pulls up a method that marries the retrieval model with deep learning (DL).

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ML Review

JUNE 4, 2018

However, in 2014 a number of high-profile AI labs began to release new approaches leveraging deep learning to improve performance. Source : Britz (2016)[ 62 ] CNNs can encode abstract features from images. 2016)[ 91 ] You et al. 2016)[ 95 ] Next we introduce the concept of adaptive attention from Lu et al.

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Heartbeat

JUNE 14, 2023

International conference on machine learning. Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks. On mixup training: Improved calibration and predictive uncertainty for deep neural networks.” Measuring Calibration in Deep Learning. References [1] Guo, Chuan, et al. “

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Heartbeat

MAY 11, 2023

The first version of YOLO was introduced in 2016 and changed how object detection was performed by treating object detection as a single regression problem. But just because we have all these YOLOs doesn’t mean that deep learning for object detection is a dormant area of research. We pay our contributors, and we don’t sell ads.

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Snorkel AI

FEBRUARY 15, 2023

Aspect Target Sentiment Food Quality Shrimp Rolls Positive Service Server Emma Negative Researchers and developers have built several modeling solutions for this task, using advanced machine learning techniques such as deep learning and neural networks. Classification.

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Snorkel AI

FEBRUARY 15, 2023

Aspect Target Sentiment Food Quality Shrimp Rolls Positive Service Server Emma Negative Researchers and developers have built several modeling solutions for this task, using advanced machine learning techniques such as deep learning and neural networks. Classification.

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AWS Machine Learning Blog

APRIL 16, 2024

It employs advanced deep learning technologies to understand user input, enabling developers to create chatbots, virtual assistants, and other applications that can interact with users in natural language. If you download the example template and deploy it, you should see that an IAM role has been created. Resources: # 1.

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Heartbeat

AUGUST 23, 2023

Natural language processing and machine learning as practical toolsets for archival processing. Natural language processing and machine learning for law and policy texts. Artificial intelligence in law: The state of play 2016. Artificial intelligence in law: The state of play 2016.

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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. The Companys net income attributable to the Company for the year ended December 31, 2016 was $4,816,000, or $0.28

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