Insight

SEPTEMBER 20, 2019

In this blog, I’ll describe how this project uses segmentation to detect lesions in an image, and classification to detect whether those lesions are benign or malignant. The box plot below shows a summary of the testing results. This shows that the model indeed learned where and what to look for in the images. on test data.

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Analytics Vidhya

JANUARY 2, 2024

Introduction In this technologically advanced era, programming languages come and go, but Python has stood the test of time, emerging as a titan in coding. Its simplicity, versatility, and robust community support have made it the go-to language for beginners and experts alike.

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Domino Data Lab

JANUARY 20, 2019

What if there was a way to quantitatively measure whether your machine learning (ML) model reflects specific domain expertise or potential bias? Testing with Concept Activation Vectors (TCAV): The Zebra. This blog post provided distilled highlights and excerpts from recent research on TCAV. Introduction.

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Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. Its value is so significant that scaling data science has become the new business imperative with organizations spending tens of millions of dollars on data, technology and talent. Data Science Techniques.

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CIO Business Intelligence

OCTOBER 13, 2023

AI researchers need to understand data and automation infrastructure, machine learning models, AI tools and algorithms, data science, programming, and how to build AI models from scratch. Deep learning is a subset of AI , and vital to the development of gen AI tools and resources in the enterprise.

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IBM Big Data Hub

FEBRUARY 13, 2024

Generative AI represents a significant advancement in deep learning and AI development, with some suggesting it’s a move towards developing “ strong AI.” Key considerations: Tech stack: Ensure your existing technology infrastructure can handle the demands of AI models and data processing.

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Dataiku

AUGUST 28, 2020

Full disclaimer: I am a bit of a data science beer geek. A few years ago, I scraped a beer rating website, and at the time, I wanted to test different recommendation algorithms. When I came across the second lab on factorization machine and deep recommendations, I remembered my old beer dataset and decided to give it a shot.

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CIO Business Intelligence

JUNE 2, 2022

Specifically, they are interested in electric utility response to cyber and physical threats, and they are working to develop an algorithm that can be used as a tested, trusted safeguard. Modern data analytics spans a range of technologies, from dedicated analytics platforms and databases to deep learning and artificial intelligence (AI).

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IBM Big Data Hub

NOVEMBER 29, 2023

They are already identifying and exploring several real-life use cases for synthetic data, such as: Generating synthetic tabular data to increase sample size and edge cases. You can combine this data with real datasets to improve AI model training and predictive accuracy. product specialists for a guided walk-through.

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Domino Data Lab

JULY 17, 2021

While more advanced models for speaker verification exist, this blog will form a basis of speech signal processing. Further, deep learning methods are built on the foundation of signal processing. NOTE : The scope of this blog is a machine learning approach to TISV. hours long from around 60 speakers.

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Cloudera

FEBRUARY 16, 2022

In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.

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Domino Data Lab

OCTOBER 7, 2020

Statistical methods for analyzing this two-dimensional data exist. MANOVA, for example, can test if the heights and weights in boys and girls is different. This statistical test is correct because the data are (presumably) bivariate normal. In this blog we show what the changes in behavior of data are in high dimensions.

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Insight

OCTOBER 25, 2019

Image credit: [link] As the title suggests, this is a story about a question that may resonate well with many machine learning practitioners trying to build applications in the real world, where clean and annotated data on a specific problem can be sparse— How do we leverage the power of AI when we have very little data?

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Domino Data Lab

APRIL 7, 2019

A good NLP library will, for example, correctly transform free text sentences into structured features (like cost per hour and is diabetic ), that easily feed into a machine learning (ML) or deep learning (DL) pipeline (like predict monthly cost and classify high risk patients ). Image Credit: Parsa Ghaffari on the Raylien Blog.

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IBM Big Data Hub

APRIL 18, 2024

Building an in-house team with AI, deep learning , machine learning (ML) and data science skills is a strategic move. Most importantly, no matter the strength of AI (weak or strong), data scientists, AI engineers, computer scientists and ML specialists are essential for developing and deploying these systems.

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IBM Big Data Hub

OCTOBER 16, 2023

Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). ML algorithms and data science are how recommendation engines at sites like Amazon, Netflix and StitchFix make recommendations based on a user’s taste, browsing and shopping cart history.

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Insight

APRIL 9, 2019

NASA [Public domain] In this blog, I’ll discuss how I worked collaboratively with various domain experts, using reinforcement learning to develop innovative solutions in rocket engine development. The biggest categories of cost for hardware designers and manufacturers are testing, verification, and calibration of their control systems.

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DataKitchen

APRIL 13, 2021

Read the complete blog below for a more detailed description of the vendors and their capabilities. This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Testing and Data Observability. Testing and Data Observability.

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Domino Data Lab

JULY 15, 2021

In the multiverse of data science, the tool options continue to expand and evolve. While there are certainly engineers and scientists who may be entrenched in one camp or another (the R camp vs. Python, for example, or SAS vs. MATLAB), there has been a growing trend towards dispersion of data science tools.

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Cloudera

SEPTEMBER 12, 2018

It was deeply gratifying to see so many organizations deploying the tools and techniques of data science and advanced analytics to solve difficult and important problems. I predict that next year’s competition will be even more amazing as we continue pushing the frontiers of data science forward. Cloud Success: .

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Cloudera

OCTOBER 17, 2017

Deep learning is in the news. But deep learning is a tool that enterprises use to solve practical problems. In this blog, we provide a few examples that show how organizations put deep learning to work. In this blog, we provide a few examples that show how organizations put deep learning to work.

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Domino Data Lab

AUGUST 9, 2021

With the right tools, your data science teams can focus on what they do best – testing, developing and deploying new models while driving forward-thinking innovation. In general terms, a model is a series of algorithms that can solve problems when given appropriate data. It’s most helpful in analyzing structured data.

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Cloudera

MAY 19, 2021

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. RAPIDS brings the power of GPU compute to standard Data Science operations, be it exploratory data analysis, feature engineering or model building.

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The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. This blog post motivates this problem more fully, and discusses monotonic splines and lattices as a solution. Curiosities and anomalies in your training and testing data become genuine and sustained loss patterns.

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Cloudera

NOVEMBER 3, 2021

Thanks to pioneers like Andrew NG and Fei-Fei Li, GPUs have made headlines for performing particularly well with deep learning techniques. Today, deep learning and GPUs are practically synonymous. While deep learning is an excellent use of the processing power of a graphics card, it is not the only use.

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Insight

JANUARY 14, 2020

Keras is an open source deep learning API that was written in Python and runs on top of Tensorflow, so it’s a little more user-friendly and high-level than Tensorflow. If you’d like to read more about all of the different optimizers out there, check out this excellent Medium article and this wonderfully detailed blog post.

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Cloudera

APRIL 27, 2023

In the blog we will cover how Ray can be used in Cloudera Machine Learning’s open-by-design architecture to bring fast distributed AI compute to CDP. Its innovative architecture enables seamless integration with ML and deep learning libraries like TensorFlow and PyTorch. This returns a provisioned Ray cluster.

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Cloudera

FEBRUARY 1, 2022

At Cloudera, we provide our customers with an extensive library of prebuilt data science projects (complete with out of the box models and apps) called Applied ML Prototypes (AMPs) to help them move the starting point of their project closer to the finish line. Take a test drive to try AMPs for yourself. Launch the AMP.

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CIO Business Intelligence

APRIL 15, 2022

Since the COVID-19 pandemic began, numerous organizations have sought to apply machine learning (ML) algorithms to help hospitals diagnose or triage patients faster. But according to the UK’s Turing Institute, a national center for data science and AI, the predictive tools made little to no difference. In a statement on Oct.

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Domino Data Lab

AUGUST 10, 2021

Models are the central output of data science, and they have tremendous power to transform companies, industries, and society. At the center of every machine learning or artificial intelligence application is the ML/AI model that is built with data, algorithms and code. It’s used for developing deep learning models.

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Domino Data Lab

APRIL 21, 2021

deep learning) there is no guaranteed explainability. In this blog post we will show an implementation of an ML-based anomaly detection based on XGBoost. We will go through a typical ML pipeline, where we do data ingestion, exploratory data analysis, feature engineering, model training and evaluation.

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BizAcuity

MARCH 31, 2022

you already have a data strategy in place, then it is easier to identify and analyze where AI would be the most useful for your business.Analytics Insight has an informative blog on the wide range of use-cases of AI in prominent industries. Do let us know by commenting below how we can improve the blog and what we can add to it.

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Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Machine learning model interpretability. back to the structure of the dataset. Riccardo Guidotti, et al.

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Domino Data Lab

JANUARY 14, 2019

This blog post provides insights on how to use the SHAP and LIME Python libraries in practice and how to interpret their output, helping readers prepare to produce model explanations in their own work. Next, we load the Boston Housing data, the same dataset we used in Part 1. Let’s build the models that we’ll use to test SHAP and LIME.

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IBM Big Data Hub

JANUARY 10, 2024

Artificial intelligence (AI) refers to the convergent fields of computer and data science focused on building machines with human intelligence to perform tasks that would previously have required a human being. For example, learning, reasoning, problem-solving, perception, language understanding and more.

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Smart Data Collective

OCTOBER 14, 2019

Regardless of if you’re a data science professional or an IT department who wants to help your company have more successful data science projects, it’s essential to have some data science tools under your belt to avail of when needed. Here are some open-source options to consider. Kubernetes.

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Sisense

AUGUST 6, 2020

AI and machine learning (ML) are not just catchy buzzwords; they’re vital to the future of our planet and your business. Be sure test cases represent the diversity of app users. Moreover, training data must conform to the app design to such an extent that data preparation resembles coding. The perfect fit.

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