Analytics Vidhya

SEPTEMBER 10, 2021

This article was published as a part of the Data Science Blogathon Introduction With ignite, you can write loops to train the network in just a few lines, add standard metrics calculation out of the box, save the model, etc. The post Training and Testing Neural Networks on PyTorch using Ignite appeared first on Analytics Vidhya.

Testing 273

Testing Metrics Data Science Publishing 273

Analytics Vidhya

APRIL 14, 2022

This article was published as a part of the Data Science Blogathon. Though in this article we will not only test the frontal face but also different angles of the image and see where our model will perform […]. The post Face Detection Using the DLIB Face Detector Model appeared first on Analytics Vidhya.

Modeling 282

Modeling Testing Data Science Publishing 282

CIO Business Intelligence

FEBRUARY 7, 2024

Key AI companies have told the UK government to speed up its safety testing for their systems, raising questions about future government initiatives that too may hinge on technology providers opening up generative AI models to tests before new releases hit the public.

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Analytics Vidhya

DECEMBER 22, 2021

This article was published as a part of the Data Science Blogathon Dear readers, In this blog, let’s build our own custom CNN(Convolutional Neural Network) model all from scratch by training and testing it with our custom image dataset.

Modeling 325

Modeling Data Science Testing Publishing 325

Analytics Vidhya

JUNE 14, 2022

This article was published as a part of the Data Science Blogathon. Background on Flower Classification Model Deep learning models, especially CNN (Convolutional Neural Networks), are implemented to classify different objects with the help of labeled images. For example, a […].

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Modeling Deep Learning Testing Data Science 208

Analytics Vidhya

JUNE 29, 2022

This article was published as a part of the Data Science Blogathon. Source: Wikipedia In this article, we shall provide some background on how multilingual multi-speaker models work and test an Indic TTS model that supports 9 languages and 17 speakers (Hindi, Malayalam, Manipuri, Bengali, Rajasthani, Tamil, Telugu, Gujarati, Kannada).

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Modeling Testing Data Science Publishing 210

Analytics Vidhya

JANUARY 11, 2021

ArticleVideos This article was published as a part of the Data Science Blogathon. Introduction The main objectives of a model validation include the testing. The post Validation of Classification Model appeared first on Analytics Vidhya.

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Modeling Testing Data Science Publishing 212

Analytics Vidhya

OCTOBER 26, 2021

This article was published as a part of the Data Science Blogathon Introduction Instance-based learning is an important aspect of supervised machine learning. The modus operandi of this algorithm is that the training examples are being stored and when the test […].

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Modeling Machine Learning Testing Data Science 256

Analytics Vidhya

JULY 9, 2022

This article was published as a part of the Data Science Blogathon. Introduction In order to build machine learning models that are highly generalizable to a wide range of test conditions, training models with high-quality data is essential.

Machine Learning 344

Machine Learning Data Science Data Collection Testing 344

Analytics Vidhya

DECEMBER 14, 2020

This article was published as a part of the Data Science Blogathon. What is a Statistical Model? “Modeling is an art, as well as. The post All about Statistical Modeling appeared first on Analytics Vidhya.

Statistics 158

Statistics Modeling Data Science Publishing 158

Analytics Vidhya

JANUARY 3, 2022

This article was published as a part of the Data Science Blogathon. Loading Data Into Big Query Training the model Evaluating the Model Testing the model Summary Shutting down the […]. Table of Contents Introduction Machine Learning Pipeline Data Preprocessing Flow of pipeline 1.

Machine Learning 353

Machine Learning Testing Data Science Publishing 353

Analytics Vidhya

AUGUST 29, 2022

This article was published as a part of the Data Science Blogathon. Introduction When training a machine learning model, the model can be easily overfitted or under fitted. To avoid this, we use regularization in machine learning to properly fit the model to our test set.

Machine Learning 290

Machine Learning Testing Data Science Publishing 290

Analytics Vidhya

NOVEMBER 19, 2021

This is article was published as a part of the Data Science Blogathon. In the model-building phase of any supervised machine learning project, we train a model with the aim to learn the optimal values for all the weights and biases from labeled examples. If we use the same labeled examples for testing our model […].

Machine Learning 305

Machine Learning Testing Data Science Optimization 305

Domino Data Lab

AUGUST 6, 2021

All models require testing and auditing throughout their deployment and, because models are continually learning, there is always an element of risk that they will drift from their original standards. As such, model governance needs to be applied to each model for as long as it’s being used.

Machine Learning 145

Machine Learning Modeling Testing Risk 145

Analytics Vidhya

SEPTEMBER 15, 2021

This article was published as a part of the Data Science Blogathon Introduction: OVERFITTING! We do not even spend a single day without encountering this situation and then try different options to get the correct accuracy of the model on the test dataset.

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Testing Data Science Publishing Modeling 286

Ontotext

APRIL 15, 2020

As all this progresses, the scientific community races against time to respond to the pandemic by developing diagnostic tests, therapies, pre-clinical and clinical research and vaccines. The FHIRCat group at the Mayo Clinic has published the CORD-19-on-FHIR dataset for COVID-19 research. The Mayo Clinic. medications: 16,406 instances.

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Publishing Metadata Data mining Technology 105

Sisense

SEPTEMBER 23, 2019

Mike Cohn’s famous Test Pyramid places API tests at the service level (integration), which suggests that around 20% or more of all of our tests should focus on APIs (the exact percentage is less important and varies based on our needs). So the importance of API testing is obvious.

Testing 89

Testing Strategy Modeling ROI 89

DataRobot

FEBRUARY 10, 2021

Even if all the code runs and the model seems to be spitting out reasonable answers, it’s possible for a model to encode fundamental data science mistakes that invalidate its results. These errors might seem small, but the effects can be disastrous when the model is used to make decisions in the real world.

Modeling 98

Modeling Deep Learning Machine Learning Testing 98

Domino Data Lab

AUGUST 9, 2021

For a model-driven enterprise, having access to the appropriate tools can mean the difference between operating at a loss with a string of late projects lingering ahead of you or exceeding productivity and profitability forecasts. What Are Modeling Tools? Importance of Modeling Tools. Types of Modeling Tools.

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Modeling Deep Learning Machine Learning Statistics 111

DataRobot

MAY 27, 2021

Michael earned his MD and PhD degrees from Emory University, and today his research combines mathematical and epidemiological models with high-throughput phage-display–based serological laboratory investigations. In particular, Mina believes that our failure to test effectively has allowed COVID-19 to spread.

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Testing Machine Learning Measurement Publishing 98

Smart Data Collective

JULY 30, 2020

In December, Springer published an insightful article about the value of deep learning for VPNs. The article “Deep learning-based real-time VPN encrypted traffic identification methods” delves into the use of machine learning to improve encryption models. It is also used for testing more effectively. What is VPN.

Machine Learning 85

Machine Learning Testing Deep Learning Advertising 85

Analytics Vidhya

MARCH 10, 2022

This article was published as a part of the Data Science Blogathon.

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Testing Data Science Publishing Modeling 217

O'Reilly on Data

OCTOBER 18, 2022

What if a named entity recognition (NER) system, based on a cutting-edge large language model (LLM), fails for Chinese, Cyrillic, or Arabic text? Audit has been an integral aspect of model risk management (MRM) in consumer finance for years, and colleagues at BLDS and QuantUniversity have been conducting model audits for some time.

Risk Management 287

Risk Management Risk Testing Measurement 287

O'Reilly on Data

JUNE 15, 2023

The world changed on November 30, 2022 as surely as it did on August 12, 1908 when the first Model T left the Ford assembly line. Today, we have dozens of organizations that publish AI principles, but they provide little detailed guidance. And they are stress testing and “ red teaming ” them to uncover vulnerabilities.

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Metrics Reporting Measurement Finance 280

Analytics Vidhya

MARCH 24, 2023

People across various walks of life have been experimenting diversely with the AI to test its knowledge and gauge its capabilities. In one such attempt, LinkedIn Co-Founder, Reid Hoffman, has published an entire book with the help of GPT-4. OpenAI’s GPT-4 is one of the most advanced LLMs today.

IT 237

IT Testing Publishing Analytics 237

DataKitchen

OCTOBER 28, 2021

This article was originally published in Forbes. Authors of an article published by McKinsey Global Institute assert that “more human vigilance is needed to critically analyze the unfair biases that can become baked in and scaled by AI systems.” What Is AI Bias?

Testing 130

Testing IT Manufacturing Metrics 130

CIO Business Intelligence

MAY 3, 2024

During pilot testing, UPS earned 50% reduction in the time agents spent resolving e-mails. The LLM gives agents the ability to confirm all responses suggested by the model. As a result, UPS is investing in training the model on its corporate data set. That frees up human agents to handle the more complex questions.”

Testing 139

Testing Sales Enterprise Digital Transformation 139

O'Reilly on Data

DECEMBER 12, 2023

If the output of a model can’t be owned by a human, who (or what) is responsible if that output infringes existing copyright? In an article in The New Yorker , Jaron Lanier introduces the idea of data dignity, which implicitly distinguishes between training a model and generating output using a model.

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Modeling Software Sales Statistics 248

O'Reilly on Data

APRIL 2, 2019

A few years ago, we started publishing articles (see “Related resources” at the end of this post) on the challenges facing data teams as they start taking on more machine learning (ML) projects. We are still in the early days for tools supporting teams developing machine learning models. Model governance.

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Machine Learning Modeling Data Science Software 201

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 83

Machine Learning Modeling Metadata Recreation/Entertainment 83

Cloudera

OCTOBER 20, 2021

The Bank of England has taken a leadership position in publishing disclosures related to its own progress on climate-related initiatives. Stress testing was heavily scrutinized in the post 2008 financial crisis. Scenarios can be analyzed and pressure tested to understand the impact of an event, before it happens.

Risk 80

Risk Modeling Risk Management Measurement 80

CIO Business Intelligence

APRIL 17, 2024

Similarly, clothing brand Under Armour recently produced an ad that used AI-generated 3D models of the British boxer Anthony Joshua, based on videos they took of him in the past. Helping software developers write and test code Similarly in tech, companies are currently open about some of their use cases, but protective of others.

Enterprise 121

Enterprise Testing Digital Transformation Software 121

CIO Business Intelligence

MARCH 7, 2024

The report, published today, centered on several individual studies of bias, including tests for associations between gendered names and careers, frequently generated less positive responses to prompts related to LGBTQ+ individuals and women, and assigned stereotyped professions to members of different genders and ethnic groups.

Testing 111

Testing Finance Reporting Measurement 111

Rocket-Powered Data Science

SEPTEMBER 25, 2022

I was asked by the publisher to provide an editorial review of the book “Building Industrial Digital Twins: Design, develop, and deploy digital twin solutions for real-world industries using Azure Digital Twins“, by Shyam Varan Nath and Pieter van Schalkwyk. For this, I received a complimentary copy of the book and no other compensation.

Diagnostic Analytics 216

Diagnostic Analytics Prescriptive Analytics Internet of Things IoT 216

DataKitchen

NOVEMBER 29, 2021

From our unique vantage point in the evolution toward DataOps automation, we publish an annual prediction of trends that most deeply impact the DataOps enterprise software industry as a whole. Model developers will test for AI bias as part of their pre-deployment testing. Companies Commit to Remote.

Testing 245

Testing Data Lake Data Architecture Manufacturing 245

DataKitchen

SEPTEMBER 20, 2021

The best way to ensure error-free execution of data production is through automated testing and monitoring. The DataKitchen Platform enables data teams to integrate testing and observability into data pipeline orchestrations. Automated tests work 24×7 to ensure that the results of each processing stage are accurate and correct.

Testing 246

Testing Metrics Measurement Dashboards 246

Analytics Vidhya

OCTOBER 29, 2021

This article was published as a part of the Data Science Blogathon Introduction to Statistics Statistics is a type of mathematical analysis that employs quantified models and representations to analyse a set of experimental data or real-world studies. Data processing is […].

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