AWS Machine Learning Blog

JUNE 23, 2023

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code.

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Flipboard

NOVEMBER 24, 2023

SnapLogic’s AI journey In the realm of integration platforms, SnapLogic has consistently been at the forefront, harnessing the transformative power of artificial intelligence. Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. Sandeep holds an MSc.

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ODSC - Open Data Science

MAY 25, 2023

Build tuned auto-ML pipelines, with common interface to well-known libraries (scikit-learn, statsmodels, tsfresh, PyOD, fbprophet, and more!) sktime, the unified package for time series ML sktime supports many time series related learning tasks and objects! Classification? Annotation? Something else?

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AWS Machine Learning Blog

FEBRUARY 1, 2024

He entered the big data space in 2013 and continues to explore that area. He is actively working on projects in the ML space and has presented at numerous conferences, including Strata and GlueCon. Consider the following picture, which is an AWS view of the a16z emerging application stack for large language models (LLMs).

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ODSC - Open Data Science

FEBRUARY 28, 2023

To deliver on their commitment to enhancing human ingenuity, SAS’s ML toolkit focuses on automation and more to provide smarter decision-making. S&P Global Last year, S&P Global Market Intelligence and IHS Markit’s Financial Services department were combined.

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Flipboard

JULY 19, 2023

In entered the Big Data space in 2013 and continues to explore that area. He is actively working on projects in the ML space and has presented at numerous conferences including Strata and GlueCon. Randy has held a variety of positions in the technology space, ranging from software engineering to product management.

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ML Review

FEBRUARY 26, 2018

ACM, 2013: 2333–2338. [2] Learning concept embeddings for query expansion by quantum entropy minimization[C]// Twenty-Eighth AAAI Conference on Artificial Intelligence. 2] Minghui Qiu and Feng-Lin Li. MeChat: A Sequence to Sequence and Rerank based Chatbot Engine. ACL 2017 [3] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio.

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Mlearning.ai

AUGUST 4, 2023

In this blog, we will try to deep dive into the concept of 1x1 convolution operation which appeared in the paper ‘Network in Network’ by Lin et al in (2013) and ‘Going Deeper with Convolutions’ by Szegedy et al (2014) that proposed the GoogLeNet architecture.

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Mlearning.ai

AUGUST 6, 2023

As described in the previous article , we want to forecast the energy consumption from August of 2013 to March of 2014 by training on data from November of 2011 to July of 2013. Experiments Before moving on to the experiments, let’s quickly remember what’s our task.

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Heartbeat

DECEMBER 14, 2023

Page: Geoffrey Hinton Summary: Geoffrey Everest Hinton (born 6 December 1947) is a British-Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. In 2017, he co-founded and became the chief scientific advisor of the Vector Institute in Toronto.With David Rumelhart and Ronald J.

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Heartbeat

DECEMBER 8, 2023

He focused on generative AI trained on large language models, The strength of the deep learning era of artificial intelligence has lead to something of a renaissance in corporate R&D in information technology, according to Yann LeCun, chief AI. Meta's chief A.I. scientist calls A.I.

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Heartbeat

DECEMBER 27, 2023

However, the emergence of the open-source Docker engine by Solomon Hykes in 2013 accelerated the adoption of the technology. The machine learning (ML) lifecycle defines steps to derive values to meet business objectives using ML and artificial intelligence (AI). What is Docker? Why Use Docker for Machine Learning?

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ML Review

MAY 3, 2018

In some senses, we are getting closer to a generalisable artificial intelligence; knowledge in deep learning is consolidating into a more paradigmatic approach. Available: [link] (last update, 18/03/2013). Such congruency allows researchers from all disciplines to leverage AI in new and exciting ways. Amodei et al.

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Mlearning.ai

JULY 31, 2023

Dosovitskiy, A., Kolesnikov, A., Weissenborn, D., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Uszkoreit, J., and Houlsby, N., An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. Available from: [link]. Mikolov, T., Corrado, G., and Dean, J., Efficient Estimation of Word Representations in Vector Space.

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Mlearning.ai

APRIL 1, 2023

It was first introduced in 2013 by a team of researchers at Google led by Tomas Mikolov. I hope that after reading this post, you will have a better understanding of how these techniques work and how they can be used to enhance natural language processing applications.

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ML Review

JUNE 4, 2018

Hence, as we shall see, attention mechanisms and reinforcement learning are at the forefront of the latest advances — and their success may one day reduce some of the decision-process opacity that harms other areas of artificial intelligence research. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.

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O'Reilly Media

APRIL 23, 2024

But in 2013 and 2014, it remained stuck at 83% , and while in the ten years since, it has reached 95% , it had become clear that the easy money that came from acquiring more users was ending. The market was maturing. From 2000 to 2011, the percentage of US adults using the internet had grown from about 60% to nearly 80%.

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ML Review

MARCH 5, 2019

In Proceedings of the Fifteenth conference on Uncertainty in Artificial Intelligence (pp. AI Distillery (Part 2): Distilling by Embedding was originally published in ML Review on Medium, where people are continuing the conversation by highlighting and responding to this story. References Harris, Z. Distributional structure.

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Flipboard

APRIL 30, 2025

Since its founding in 2013, FloQast has had the privilege of working with over 2,800 organizations across various industries and regions, helping them streamline their accounting operations. Aidan Anderson is a dynamic technology leader with over a decade of experience in software engineering, security, and artificial intelligence.

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