AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

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Towards AI

FEBRUARY 27, 2024

Looking ahead, it has served the ML community a lot while building different Natural Language Understanding tools and models as a high-quality curated corpus of information. The open-source movement gained hold with the rise of the Internet, and it has since grown into a vibrant scene with many contributors and projects.

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AWS Machine Learning Blog

MARCH 29, 2023

there is enormous potential to use machine learning (ML) for quality prediction. ML-based predictive quality in HAYAT HOLDING HAYAT is the world’s fourth-largest branded baby diapers manufacturer and the largest paper tissue manufacturer of the EMEA. With the ongoing digitization of the manufacturing processes and Industry 4.0,

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Dataconomy

APRIL 18, 2023

Undetectable backdoors can be implemented in any ML algorithm Machine learning Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and models that can learn from data and make predictions or decisions.

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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. Defining hyperparameters involves setting the values for various parameters used during the training process of an ML model.

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ODSC - Open Data Science

OCTOBER 4, 2024

His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, and deep reinforcement learning algorithms.

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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. “On

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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

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. Introducing ?️YOLO-NAS:

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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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ODSC - Open Data Science

SEPTEMBER 22, 2023

Most recently, Gaurav served as VP of Product at Neural Magic — innovators in software acceleration for deep learning utilizing sparse model architectures. Before founding Gretel, he launched Harvest.ai, a security startup that leveraged NLP and AI to protect cloud data, which was acquired by Amazon in 2016.

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Heartbeat

JUNE 14, 2023

On mixup training: Improved calibration and predictive uncertainty for deep neural networks.” Measuring Calibration in Deep Learning. Editorially independent, Heartbeat is sponsored and published by Comet, an MLOps platform that enables data scientists & ML teams to track, compare, explain, & optimize their experiments.

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Mlearning.ai

JUNE 16, 2023

The common practice for developing deep learning models for image-related tasks leveraged the “transfer learning” approach with ImageNet. ML practitioners, believing they had to match the sheer size of ImageNet, refrained from pre-training with much smaller available medical image datasets, let alone developing new ones.

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DrivenData Labs

DECEMBER 13, 2022

head () Out[4]: uid latitude longitude date split 0 aabm 39.080319 -86.430867 2018-05-14 train 1 aabn 36.559700 -121.510000 2016-08-31 test 2 aacd 35.875083 -78.878434 2020-11-19 train 3 aaee 35.487000 -79.062133 2016-08-24 train 4 aaff 38.049471 -99.827001 2019-07-23 train In [5]: metadata. In [4]: metadata = pd. value_counts ().

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Mlearning.ai

MARCH 9, 2023

Recent studies have demonstrated that deep learning-based image segmentation algorithms are vulnerable to adversarial attacks, where carefully crafted perturbations to the input image can cause significant misclassifications (Xie et al., Towards deep learning models resistant to adversarial attacks. 2018; Sitawarin et al.,

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Heartbeat

SEPTEMBER 13, 2023

For example, explainability is crucial if a healthcare professional uses a deep learning model for medical diagnoses. Captum allows users to explain both deep learning and traditional machine learning models. Explainability in Machine Learning || Seldon Blazek, P. Russell, C. & & Watcher, S.

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Mlearning.ai

APRIL 10, 2023

Supervised machine learning (such as SVM or GradientBoost) and deep learning models (such as CNN or RNN) can promise far superior performances when comparing them to clustering models however this can come at a greater cost with marginal rewards to the environment, end-user, and product owner of such technology. 2016.2545384.

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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. Defining hyperparameters involves setting the values for various parameters used during the training process of an ML model.

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Heartbeat

AUGUST 23, 2023

Natural language processing and machine learning for law and policy texts. Artificial intelligence in law: The state of play 2016. Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

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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.

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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.

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Mlearning.ai

FEBRUARY 15, 2023

Deep learning models with multilayer processing architecture are now outperforming shallow or standard classification models in terms of performance [5]. Deep ensemble learning models utilise the benefits of both deep learning and ensemble learning to produce a model with improved generalisation performance.

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Mlearning.ai

MARCH 2, 2023

Conclusion: BERT as Trend-Setter in NLP and Deep Learning References I. Preliminaries: Transformers and Unsupervised Transfer Learning This section presents the most important theoretical background to understand BERT. Benchmark Results V. Contributions of BERT V.1 1 Impact V.2 3 Applications VI. arXiv:1804.07461. [10]

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Heartbeat

FEBRUARY 2, 2024

Deep residual learning for image recognition. Editor's Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners. Ren, S., & Sun, J.

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DagsHub

DECEMBER 16, 2024

In the fraud detection example, to effectively translate ML metrics into business-relevant information, we need to understand how the model’s behavior impacts business objectives. In other words, the outcomes of ML models should be equitable across different groups, even if there were historical biases in the training data.

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Snorkel AI

MAY 3, 2023

This is a model trained on the Berkeley Deep Drive-100k dataset , performing bounding box tracking on the road. Whenever we see videos like this, we may get this overly positive impression of how remarkable deep learning models are, which is true in some cases. Learn more, live!

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Snorkel AI

MAY 3, 2023

This is a model trained on the Berkeley Deep Drive-100k dataset , performing bounding box tracking on the road. Whenever we see videos like this, we may get this overly positive impression of how remarkable deep learning models are, which is true in some cases. Learn more, live!

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Dataconomy

MAY 15, 2023

In 2016, she began her career in social media by going live on YouNow. With the help of NLP, ML, and CV, these AI girlfriends can grow to understand and appreciate their users’ individual tastes and quirks. The software tailors its chat with you using NLP and ML to make it feel natural and interesting.

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ML Review

MAY 3, 2018

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. While an oversimplification, the generalisability of current deep learning approaches is impressive.

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DrivenData Labs

MARCH 30, 2023

His interests are in privacy-preserving machine learning, particularly in the areas of differential privacy, ML security, and federated learning. Shengyuan is a PhD student at Carnegie Mellon University working with Virginia Smith with expertise in federated learning and differential privacy.

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DrivenData Labs

SEPTEMBER 10, 2024

Many teams combined technical skills in AI/ML with domain knowledge in neuroscience, aging, or healthcare. Paola Ruíz Puente is a Biomedical Engineer amd the AI/ML manager at IGC Pharma. Pablo Arbeláez is a distinguished researcher with over 20 years of experience using AI/ML in medicine, biology, and computer vision.

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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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Mlearning.ai

APRIL 28, 2023

Math Datasets The authors the Toolformers' mathematical reasoning abilities on ASDiv ( Miao et al., 2020 ), SVAMP ( Patel et al., 2021 ), and the MAWPS benchmark ( Koncel-Kedziorski et al.,

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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. Given the importance of Jupyter to data scientists and ML developers, AWS is an active sponsor and contributor to Project Jupyter.

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Mlearning.ai

FEBRUARY 10, 2023

Back in 2016 I was trying to explain to software engineers how to think about machine learning models from a software design perspective; I told them that they should think of a database. Both serve as a means of storing representations of historical data, which can later be queried. Thanks to Pedro Lapietra for co-editing.

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