Dataconomy

JUNE 8, 2023

These technologies leverage sophisticated algorithms to process vast amounts of medical data, helping healthcare professionals make more accurate decisions. By leveraging machine learning algorithms, AI systems can analyze medical images, such as X-rays, MRIs, and CT scans, with remarkable accuracy and speed.

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Dataconomy

FEBRUARY 6, 2024

Artificial intelligence and machine learning are no longer the elements of science fiction; they’re the realities of today. This popularity is primarily due to the spread of big data and advancements in algorithms. Machine learning algorithms are designed to uncover connections and patterns within data.

ML 121

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AWS Machine Learning Blog

MARCH 15, 2024

Many organizations are implementing machine learning (ML) to enhance their business decision-making through automation and the use of large distributed datasets. Because this data is across organizations, we use federated learning to collate the findings. He entered the big data space in 2013 and continues to explore that area.

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Flipboard

MAY 30, 2025

Throughout her career, she has shared her expertise at numerous conferences and has authored several blogs in the Machine Learning and Generative AI domains. She works on developing solutions for Responsible AI, focusing on algorithmic fairness, veracity of large language models, and explainability.

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ODSC - Open Data Science

FEBRUARY 28, 2023

Improving Operations and Infrastructure Taipy The inspiration for this open-source software for Python developers was the frustration felt by those who were trying, and struggling, to bring AI algorithms to end-users. Cloudera For Cloudera, it’s all about machine learning optimization.

Data Science 96

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Heartbeat

DECEMBER 27, 2023

Photo by Ian Taylor on Unsplash This article will comprehensively create, deploy, and execute machine learning application containers using the Docker tool. It will further explain the various containerization terms and the importance of this technology to the machine learning workflow. What is Docker?

Machine Learning 52

Machine Learning Machine Learning Python ML 52

PyImageSearch

NOVEMBER 13, 2023

For example, image classification, image search engines (also known as content-based image retrieval, or CBIR), simultaneous localization and mapping (SLAM), and image segmentation, to name a few, have all been changed since the latest resurgence in neural networks and deep learning. 2015 ), SSD ( Fei-Fei et al., probability).

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Pickl AI

FEBRUARY 23, 2025

These tools leverage advanced algorithms and methodologies to process large datasets, uncovering valuable insights that can drive strategic decision-making. uses Hadoop to process over 24 petabytes of data daily, enabling them to improve their search algorithms and ad targeting. Use Cases : Yahoo!

Big Data 52

Big Data Big Data Apache Hadoop Apache Kafka 52

Towards AI

MAY 12, 2025

A Brief History of Static Embeddings The concept of static embeddings gained popularity with the introduction of Word2Vec by researchers at Google in 2013. It provides a fast and memory-efficient implementation of various NLP algorithms, including Word2Vec, Doc2Vec, and FastText.

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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. Choose Add step.

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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., The MBD algorithm then searches for a subset of nodes (i.e., 2013; Goodfellow et al., 2012; Otsu, 1979; Long et al., 2018; Sitawarin et al.,

Deep Learning 52

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Towards AI

JULY 19, 2023

Basically crack is a visible entity and so image-based crack detection algorithms can be adapted for inspection. Deep learning algorithms can be applied to solving many challenging problems in image classification. Deep learning algorithms can be applied to solving many challenging problems in image classification.

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Flipboard

NOVEMBER 24, 2023

Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline. Since joining SnapLogic in 2010, Greg has helped design and implement several key platform features including cluster processing, big data processing, the cloud architecture, and machine learning.

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Mlearning.ai

MAY 18, 2023

In the Beginning The first object detection algorithm is difficult to pinpoint to a single specific algorithm, as the field of object detection has evolved over several decades with numerous contributions. The development of region-based convolutional neural networks (R-CNN) in 2013 marked a crucial milestone.

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AWS Machine Learning Blog

MAY 24, 2023

In this three-part series, we present a solution that demonstrates how you can automate detecting document tampering and fraud at scale using AWS AI and machine learning (ML) services for a mortgage underwriting use case. You can use the prediction to trigger business rules in relation to underwriting decisions.

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ODSC - Open Data Science

FEBRUARY 29, 2024

EVENT — ODSC East 2024 In-Person and Virtual Conference April 23rd to 25th, 2024 Join us for a deep dive into the latest data science and AI trends, tools, and techniques, from LLMs to data analytics and from machine learning to responsible AI. Another project, SynthID , helps to identify and watermark AI-generated images.

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phData

MAY 17, 2024

Cortex ML is Snowflake’s newest feature, added to enhance the ease of use and low-code functionality of your business’s machine learning needs. The forecasting algorithm uses gradient boosting to model data and the rolling average of historical data to help predict trends. What is Cortex ML, and Why Does it Matter?

ML 52

ML ML Predictive Analytics Machine Learning 52

Pickl AI

JULY 12, 2023

Numerous movies have been produced and made that enables you to understand the ways in which Artificial Intelligence, Machine Learning, Data and Information have played crucial roles. The Matrix is mainly a world simulated for creating and controlling machines, which use data and algorithms to maintain the illusion of reality.

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence Data Analysis 52

Towards AI

APRIL 8, 2024

Word2Vec, a widely-adopted NLP algorithm has proven to be an efficient and valuable tool that is now applied across multiple domains, including recommendation systems. While numerous techniques have been explored, methods harnessing natural language processing (NLP) have demonstrated strong performance.

Natural Language Processing 80

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Heartbeat

DECEMBER 14, 2023

From 2013 to 2023, he divided his time working for Google (Google Brain) and the University of Toronto, before publicly announcing his departure from Google in May 2023 citing concerns about the risks of artificial intelligence (AI) technology. Hinton is viewed as a leading figure in the deep learning community.

Deep Learning 52

Deep Learning Deep Learning Machine Learning Machine Learning 52

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 8, 2023

This includes cleaning and transforming data, performing calculations, or applying machine learning algorithms. Hinton is viewed as a leading figure in the deep learning community. Data processing and manipulation: Tools provide the necessary functionality for an agent to process and manipulate data.

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Snorkel AI

FEBRUARY 6, 2024

If you think about simple ingredients in the “cooking show” of AI—models, algorithms, and data—a lot of the models and algorithms have gotten a lot more black box; a lot more commoditized; frankly, available in the most positive sense. Yes, people were aware of data science, they were aware of machine learning.

Data Science 52

Data Science Machine Learning Machine Learning AI 52

Mlearning.ai

JULY 31, 2023

The repository includes embedding algorithms, such as Word2Vec, GloVe, and Latent Semantic Analysis (LSA), to use with their PIP loss implementation. As such, I’ve adapted and converted the simplest algorithm (LSA) and PIP loss implementations with PyTorch and guided comments for more flexibility. Dosovitskiy, A., Kolesnikov, A.,

Deep Learning 52

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Snorkel AI

FEBRUARY 6, 2024

If you think about simple ingredients in the “cooking show” of AI—models, algorithms, and data—a lot of the models and algorithms have gotten a lot more black box; a lot more commoditized; frankly, available in the most positive sense. Yes, people were aware of data science, they were aware of machine learning.

Data Science 52

Data Science Machine Learning Machine Learning Data Scientist 52

ML Review

JUNE 4, 2018

Finally, one can use a sentence similarity evaluation metric to evaluate the algorithm. One such evaluation metric is the Bilingual Evaluation Understudy algorithm, or BLEU score. The idea of sampling an attention trajectory as an estimation was taken from a Reinforcement Learning algorithm called REINFORCE[88].

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Deep Learning Deep Learning Algorithm Machine Learning 52

ML Review

MAY 3, 2018

The arrival of Deep Learning It is with this point that we introduce recent work from Assael et al. 17] “ LipNet ” introduces the first approach for an end-to-end lip reading algorithm at sentence level. Deep Learning est en train de mourir. Thus the algorithm is alignment-free. Vive Differentiable Programming! [27]

Deep Learning 52

Deep Learning Deep Learning Natural Language Processing Machine Learning 52

PyImageSearch

OCTOBER 2, 2023

VAEs were introduced in 2013 by Diederik et al. They extended the idea of autoencoders to learn useful data distributions. It serves as a direct drop-in replacement for the original Fashion-MNIST dataset for benchmarking machine learning algorithms, with the benefit of being more representative of the actual data tasks and challenges.

Deep Learning 122

Deep Learning Deep Learning Clustering Computer Science 122

Pickl AI

MARCH 7, 2025

It includes AI, Deep Learning, Machine Learning and more. High Demand for Data Scientists: Data Science roles have grown over 250% since 2013, with salaries reaching $153k/year. AI and Machine Learning Integration: AI-driven Data Science powers industries like healthcare, e-commerce, and entertainment34.

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Data Science Machine Learning Machine Learning Data Scientist 52

Hacker News

MARCH 18, 2025

And so were in a position to compare the results of human effort (aided, in many cases, by systematic search) with what we can automatically do by the algorithmic process of adaptive evolution. Butas was actually already realized in the mid-1990sits still possible to use algorithmic methods to fill in pieces of patterns.

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Algorithm Machine Learning Machine Learning Data Science 169

ML Review

MARCH 5, 2019

Word embeddings Visualisation of word embeddings in AI Distillery Word2vec is a popular algorithm used to generate word representations (aka embeddings) for words in a vector space. For instance, consider the sentence “ I like machine learning ” and a context window of size 1. References Harris, Z. Distributional structure.

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Flipboard

JANUARY 20, 2025

Anand, who began as an analyst in 2013, was promoted to assistant vice president in 2015. As an assistant vice president, he developed data science and machine learning models to price bonds more accurately. The existing algorithms were not efficient. There are eight of what he calls spokes in data science.

Data Science 144

Data Science Data Scientist Exploratory Data Analysis Machine Learning 144

DrivenData Labs

DECEMBER 7, 2023

In the Unsupervised Wisdom Challenge , participants were tasked with identifying novel, effective methods of using unsupervised machine learning to extract insights about older adult falls from narrative medical record data. I enjoy participating in machine learning/data-science challenges and have been doing it for a while.

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