Insight

MAY 16, 2019

It is a high-level, multifaceted field that allows machines to iteratively learn and understand complex representations from images and videos to automate human visual tasks. Pinterest developed a visual search engine which uses an object detection pipeline for content recommendation. Transfer Learning?—?YOLO.

Deep Learning 84

Deep Learning Machine Learning Metadata Data Science 84

Domino Data Lab

JULY 28, 2021

Data science is an exciting, interdisciplinary field that is revolutionizing the way companies approach every facet of their business. Data Science — A Venn Diagram of Skills. Data science encapsulates both old and new, traditional and cutting-edge. 3 Components of Data Science Skills.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

IBM Big Data Hub

AUGUST 11, 2023

These insights can help drive decisions in business, and advance the design and testing of applications. Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights.

Data Science 59

Data Science Machine Learning Cost-Benefit Deep Learning 59

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. Introduction.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

CIO Business Intelligence

JUNE 7, 2022

Predictive analytics is often considered a type of “advanced analytics,” and frequently depends on machine learning and/or deep learning. Prescriptive analytics is a type of advanced analytics that involves the application of testing and other techniques to recommend specific solutions that will deliver desired outcomes.

Data Analytics 129

Data Analytics Diagnostic Analytics Management Analytics 129

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.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

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.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

Domino Data Lab

JULY 9, 2019

Chris Wiggins , Chief Data Scientist at The New York Times, presented “Data Science at the New York Times” at Rev. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. Data science. Session Summary.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

O'Reilly on Data

MARCH 7, 2019

Language understanding benefits from every part of the fast-improving ABC of software: AI (freely available deep learning libraries like PyText and language models like BERT ), big data (Hadoop, Spark, and Spark NLP ), and cloud (GPU's on demand and NLP-as-a-service from all the major cloud providers). IBM Watson NLU.

Deep Learning 227

Deep Learning Testing Machine Learning Modeling 227

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

O'Reilly on Data

DECEMBER 12, 2019

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. It’s a very simple and powerful idea: simulate data that you find interesting and see what a model predicts for that data. 6] Debugging may focus on a variety of failure modes (i.e.,

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Domino Data Lab

MARCH 16, 2021

DeepLIFT was recently proposed as a recursive prediction explanation method for deep learning [8, 7]. Allows a researcher to test the importance of high-level, human interpretable concepts in their network. This is an exciting and important area of data science research. Saliency Maps. Image source: [link].

Modeling 122

Modeling Deep Learning Testing Optimization 122

DataRobot

OCTOBER 20, 2021

What Is Data Literacy? Data literacy is the ability to understand data science and AI applications critically using basic data visualization, communication, and reasoning skills. And beyond uniting diverse stakeholders, data literacy also empowers sectoral regulators to provide industry-specific guidance.

Machine Learning 52

Machine Learning Metrics Risk Deep Learning 52

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?

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

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. ML model interpretability and data visualization.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

For example, consider the following simple example fitting a two-dimensional function to predict if someone will pass the bar exam based just on their GPA (grades) and LSAT (a standardized test) using the public dataset (Wightman, 1998). Curiosities and anomalies in your training and testing data become genuine and sustained loss patterns.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

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.

Cost-Benefit 97

Cost-Benefit Modeling Manufacturing Interactive 97

Domino Data Lab

OCTOBER 14, 2020

We’ve developed a model-driven software platform, called Climate FieldView , that captures, visualizes, and analyzes a vast array of data for farmers and provides new insight and personalized recommendations to maximize crop yield. for dset, data in mnist_data.items(): dset_output_url = '{}/{}'.format(output_url, Why Petastorm?

Experimentation 99

Experimentation Modeling Deep Learning Testing 99

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. A lot of testing AI methods can be utilized for better and more accurate outcomes from mining the data.

Data Analytics 52

Data Analytics Analytics Prescriptive Analytics Predictive Analytics 52

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Welcome back to our monthly series about data science! This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Grooper

AUGUST 19, 2019

An interview with Pranshuk Kathed, machine and deep learning enthusiast. Data scientists are a precious resource, so I thought I'd ask some basic questions to try and shed a little light on the basics. Everyone says you spend most of your time prepping data. It’s true, 80% of my time goes into data munching.

Machine Learning 40

Machine Learning Metrics Descriptive Analytics Testing 40

Domino Data Lab

JUNE 23, 2019

Be aware that machine learning often involves working on something that isn’t guaranteed to work. As a result, Skomoroch advocates getting “designers and data scientists, machine learning folks together and using real data and prototyping and testing” as quickly as possible. Testing is critical. Transcript.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

APRIL 3, 2019

The top three items are essentially “the devil you know” for firms which want to invest in data science: data platform, integration, data prep. Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. Rinse, lather, repeat.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

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 on the Cloudera Data Platform comes pre-configured with all the necessary libraries and dependencies to bring the power of RAPIDS to your projects. Ingest Data.

Machine Learning 136

Machine Learning Data Science Data Lake Modeling 136

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

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.

Deep Learning 108

Deep Learning Machine Learning Modeling Data Science 108

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.

Data Science 68

Data Science Machine Learning Big Data Interactive 68

Alation

APRIL 14, 2022

A useful feature for exposing patterns in the data. Visual Profiling. Supports the ability to interact with the actual data and perform analysis on it. Automatic sampling to test transformation. The vision of big data freed organizations to capture more data sources at lower levels of detail and in vastly greater volumes.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Smart Data Collective

NOVEMBER 9, 2022

There are a number of great applications of machine learning. One of the biggest benefits is testing processes for optimal effectiveness. The main purpose of machine learning is to partially or completely replace manual testing. One example is using machine learning tools like Selenium to test web development processes.

Machine Learning 98

Machine Learning Testing Cost-Benefit Data-driven 98