Sisense

APRIL 10, 2020

It’s quite popular for its visualizations: charts, graphs, pictures, and various plots. These visualizations are useful for helping people visualize and understand trends , outliers, and patterns in data. These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

DataRobot

NOVEMBER 9, 2021

When we convert the single channel audio signal time series into an energy spectrogram, it allows us to run state of the art deep learning architectures on the image. . DataRobot’s Visual AI models in autopilot built a model with an improved 1 percent better performance without any additional parameter tuning.

Visualization 52

Visualization Unstructured Data Statistics Metrics 52

DataRobot Blog

APRIL 22, 2022

In our previous post , we talked about how red AI means adding computational power to “buy” more accurate models in machine learning , and especially in deep learning. We covered different ways of measuring model efficiency and showed ways to visualize this and select models based on it. Conclusion.

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

Cloudera

FEBRUARY 25, 2021

The work of a machine learning model developer is highly complex. They need strong data exploration and visualization skills, as well as sufficient data engineering chops to fix the gaps they find in their initial study. Here’s a preview of what you can leverage with one click in CML: Deep Learning for Anomaly Detection.

Deep Learning 84

Deep Learning Machine Learning Forecasting Visualization 84

Smart Data Collective

JULY 1, 2023

There are a lot of image annotation techniques that can make the process more efficient with deep learning. This he’s just one of the many ways that artificial intelligence has significantly improved outcomes that rely on visual media. Consistency and agreement Establish an agreement metric (e.g.,

Machine Learning 93

Machine Learning Metrics Uncertainty Deep Learning 93

Domino Data Lab

SEPTEMBER 9, 2019

Next let’s use the displaCy library to visualize the parse tree for that sentence: In [4]: from spacy import displacy?? The displaCy library provides an excellent way to visualize named entities: In [15]: displacy.render(doc, style="ent"). lemma – a root form of the word. part of speech. for a, b in pairs:?.

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

More knots make the learned feature transformation smoother and more capable of approximating any monotonic function. As a result, selecting knots according to the quantiles of the input data (or even linearly across the domain), and then steadily increasing their number as long as the metrics improve works well in practice.

Machine Learning 111

Machine Learning Deep Learning Modeling Interactive 111

AWS Big Data

JUNE 26, 2023

We use Amazon Neptune to visualize the customer data before and after the merge and harmonization. We use Neptune to visualize the customer data before and after the merge and harmonization to see how the transform FindMacthes can bring all related customer data together to get a complete customer 360 view.

Insurance 98

Insurance Visualization Data Lake Metrics 98

CIO Business Intelligence

AUGUST 21, 2024

The flashpoint moment is that rather than being based on rules, statistics, and thresholds, now these systems are being imbued with the power of deep learning and deep reinforcement learning brought about by neural networks,” Mattmann says. Adding smarter AI also adds risk, of course.

Risk 136

Risk Insurance Cost-Benefit Software 136

Business Over Broadway

DECEMBER 20, 2018

Top Machine Learning and Data Science Methods Used at Work – The practice of data science requires the use algorithms and data science methods to help data professionals extract insights and value from data. Looking ahead to 2018, data professionals are most interested in learning deep learning (41%).

Machine Learning 74

Machine Learning Data Science Deep Learning Predictive Analytics 74

Insight

MARCH 4, 2020

Clustering result on vectors from TF-IDF (2D visualization from UMAP) BERT sentence embedding I then tried using sentence embedding models (BERT) to embed reviews into a vector space where the vectors capture the contextual meaning of sentences. Let’s do a quick deep dive into the model design.

Visualization 81

Visualization Metrics Modeling Machine Learning 81

Cloudera

SEPTEMBER 20, 2022

A “comic book” in this context is a story told visually through a series of images, and optionally (though often) in conjunction with written language, e.g., in speech bubbles or as captions. Deep learning models are data hungry, and state-of-the-art systems like DALL·E 2 are trained with massive data sets of images scraped from the internet.

Deep Learning 88

Deep Learning Cost-Benefit Risk Data-driven 88

DataRobot

OCTOBER 20, 2021

Data literacy is the ability to understand data science and AI applications critically using basic data visualization, communication, and reasoning skills. rule-based AI , machine learning , deep learning , etc.) What Is Data Literacy?

Machine Learning 52

Machine Learning Metrics Deep Learning Risk 52

Domino Data Lab

JUNE 23, 2019

Companies with successful ML projects are often companies that already have an experimental culture in place as well as analytics that enable them to learn from data. Ensure that product managers work on projects that matter to the business and/or are aligned to strategic company metrics. That’s another pattern.

Management 110

Management Machine Learning Experimentation Metrics 110

IBM Big Data Hub

JULY 6, 2023

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics. Python is the most common programming language used in machine learning.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

Insight

OCTOBER 25, 2019

The latter is particularly restricting, as it violates the prerequisite of many deep learning methods for image classification?—?a They start with different strategies to transform 3-D into 2-D, followed by distinct machine learning approaches. One being the 3-D nature of our images. a large number of training images.

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

IBM Big Data Hub

DECEMBER 20, 2023

The most common unsupervised learning method is cluster analysis, which uses clustering algorithms to categorize data points according to value similarity (as in customer segmentation or anomaly detection ). To deploy reinforcement learning, an agent takes actions in a specific environment to reach a predetermined goal.

Machine Learning 73

Machine Learning Modeling Deep Learning Predictive Modeling 73

DataRobot Blog

MAY 26, 2022

However, with the widespread adoption of modern ML techniques, including gradient-boosted decision trees (GBDTs) and deep learning algorithms , many traditional validation techniques become difficult or impossible to apply.

Risk 52

Risk Modeling Metrics Business Objectives 52

IBM Big Data Hub

DECEMBER 19, 2023

Anomaly detection simply means defining “normal” patterns and metrics—based on business functions and goals—and identifying data points that fall outside of an operation’s normal behavior. Unsupervised learning Unsupervised learning techniques do not require labeled data and can handle more complex data sets.

Machine Learning 64

Machine Learning Optimization Unstructured Data Sales 64

DataRobot Blog

FEBRUARY 14, 2022

Because our dataset contains image data, DataRobot used models that contain deep learning based image featurizers. Typically this means finding out how many predictions have been made, how many requests have been made to the deployment, and other performance-related metrics. Activation Maps.

Machine Learning 52

Machine Learning Metrics Sales Modeling 52

Domino Data Lab

MARCH 3, 2019

What metrics are used to evaluate success? O’Reilly Media had an earlier survey about deep learning tools which showed the top three frameworks to be TensorFlow (61% of all respondents), Keras (25%), and PyTorch (20%)—and note that Keras in this case is likely used as an abstraction layer atop TensorFlow.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

IBM Big Data Hub

AUGUST 28, 2023

Machine learning algorithms like Naïve Bayes and support vector machines (SVM), and deep learning models like convolutional neural networks (CNN) are frequently used for text classification. If necessary, make adjustments to the preprocessing, representation and/or modeling steps to improve the results.

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

Smarten

APRIL 19, 2024

Anomaly Alerts KPI monitoring and Auto Insights allows business users to quickly establish KPIs and target metrics and identify the Key Influencers and variables for the target KPI.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Grooper

AUGUST 19, 2019

An interview with Pranshuk Kathed, machine and deep learning enthusiast. For simple reporting projects, I might spend 8 hours getting the right data and then just a couple of hours producing the needed visualizations. What are the metrics that business wants to see and why it is valuable? Were you prepared for that?

Machine Learning 40

Machine Learning Metrics Descriptive Analytics Testing 40

IBM Big Data Hub

AUGUST 11, 2023

MLOps is the next evolution of data analysis and deep learning. Simply put, MLOps uses machine learning to make machine learning more efficient. Generative AI is a type of deep-learning model that takes raw data, processes it and “learns” to generate probable outputs.

Data Science 53

Data Science Machine Learning Deep Learning Cost-Benefit 53

Insight

JANUARY 14, 2020

CNNs have been widely considered state-of-the-art tools for computer vision since 2012, when AlexNet won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). 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.

Metrics 58

Metrics Optimization Modeling Testing 58

Domino Data Lab

JULY 28, 2021

Communication cannot be emphasized enough, for it is this trait that ensures results are effectively translated from the white board to impact on business metrics. In fact, deep learning was first described theoretically in 1943. None of these techniques are new.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

Insight

MARCH 28, 2019

Visualize Glassdoor reviews from your dream companies Yanxia Li built CompanySnapshot, a tool to help individuals visualize Glassdoor employee reviews, reducing the time needed to thoroughly review a company’s Glassdoor profile. xPea allows users to discover these and other surprising matches as they search for their dream jobs.

Dashboards 70

Dashboards Data Science Visualization Machine Learning 70

DataRobot Blog

DECEMBER 20, 2022

DataRobot also processes nearly every type of data , such as satellite and street imagery of real estate properties using DataRobot Visual AI , the latitude and longitude of properties and nearby city’s points of interest using DataRobot Location AI , tweets, and reviews with geotagged locations using DataRobot Text AI.

Management 59

Management Machine Learning Optimization Modeling 59

O'Reilly on Data

APRIL 19, 2021

First, 82% of the respondents are using supervised learning, and 67% are using deep learning. Deep learning is a set of algorithms that are common to almost all AI approaches, so this overlap isn’t surprising. 58% claimed to be using unsupervised learning. Techniques.

Enterprise 364

Enterprise Risk Modeling Manufacturing 364

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Domino Data Lab

MAY 8, 2019

On the other hand, as Lipton emphasized, while the tooling produces interesting visualizations, visualizations do not imply interpretation. ML model interpretability and data visualization. From my experiences leading data teams, when a business is facing difficult challenges, data visualizations can help or hurt.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

AUGUST 29, 2019

This article provides an excerpt “Deep Reinforcement Learning” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The article includes an overview of reinforcement learning theory with focus on the deep Q-learning. metrics simultaneously.

Optimization 111

Optimization Deep Learning Modeling Metrics 111

Domino Data Lab

JULY 22, 2019

He also really informed a lot of the early thinking about data visualization. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. They’d like to do something more efficient when they’re training a lot of deep learning models.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

CIO Business Intelligence

MAY 6, 2025

These are your standard reports and dashboard visualizations of historical data showing sales last quarter, NPS trends, operational thoughts or marketing campaign performance. Todays self-service platforms enable business users to slice and dice data, create visualizations and build basic predictive models.

Prescriptive Analytics 78

Prescriptive Analytics Predictive Analytics Descriptive Analytics ROI 78