DataRobot

APRIL 6, 2020

If you’re a regular reader of the DataRobot blog, you likely fall into one of two categories. Our blogs target these two groups because they are the primary people who interact with our software. Until now.

Deep Learning 69

Deep Learning Forecasting Data Science Interactive 69

Cloudera

JANUARY 7, 2021

And in the world of e-commerce, assigning product descriptions to the most fitting product category ensures quality control. . Very few real-world use cases have categories that are eternally set in stone. The reality is that new classes, topics, and categories will likely emerge over time as business needs evolve.

Machine Learning 105

Machine Learning Deep Learning IT 105

Laminar Security

DECEMBER 13, 2023

Machine learning algorithms can be trained to recognize patterns in the data and classify data accordingly. For example, an AI system could be trained to classify emails into categories like “sensitive” or “restricted” based on patterns it has learned from a training dataset.

Data-driven 119

Data-driven Machine Learning Risk Deep Learning 119

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. In this blog post, we describe how we impose common-sense “shape constraints” on complex models.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Cloudera

AUGUST 20, 2020

The imperative to deliver meaningful change and value through innovation is why the Data for Enterprise AI category at the Data Impact Awards has never been more of the moment than it is today. UOB used deep learning to improve detection of procurement fraud, thereby fighting financial crime. We hope to see your entry next year!

Enterprise 121

Enterprise Experimentation Uncertainty Deep Learning 121

Cloudera

OCTOBER 27, 2020

Our goal is to take the incredible data science and machine learning research developments we see emerging from academia and large industrial labs, and bridge the gap to products and processes that are useful to practitioners working across industries. Evolving Research At Cloudera Fast Forward. Structural Time Series.

Deep Learning 59

Deep Learning Machine Learning Forecasting Uncertainty 59

Smart Data Collective

SEPTEMBER 23, 2020

This blog will reveal or show the difference between the data warehouse and the data lake. This is true when it comes to deep learning that needs scalability in the growing number of training information. However, a data lake functions for one specific company, the data warehouse, on the other hand, is fitted for another.

Data Lake 105

Data Lake Data Warehouse Unstructured Data Big Data 105

Domino Data Lab

JULY 15, 2021

Key categories of tools and a few examples include: Data Sources. Languages are typically broken into two categories, commercial and open source. Originally created for software development, Python is used in a variety of contexts, including deep learning research and model deployment. They range from flat files (e.g.

Data Science 145

Data Science Visualization Modeling Enterprise 145

IBM Big Data Hub

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. This blog post will clarify some of the ambiguity. Machine learning is a subset of AI.

Deep Learning 61

Deep Learning Machine Learning Unstructured Data Modeling 61

DataKitchen

APRIL 13, 2021

Read the complete blog below for a more detailed description of the vendors and their capabilities. Because it is such a new category, both overly narrow and overly broad definitions of DataOps abound. Download the 2021 DataOps Vendor Landscape here. DataOps is a hot topic in 2021.

Testing 307

Testing Machine Learning Consulting Data Quality 307

Occam's Razor

MARCH 30, 2017

People tend to use these phrases almost interchangeably: Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning. Deep Learning is a specific ML technique. Most Deep Learning methods involve artificial neural networks, modeling how our bran works.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

IBM Big Data Hub

OCTOBER 16, 2023

Filtering email ML algorithms in Google’s Gmail automate filtering customers’ email into Primary, Social and Promotions categories while also detecting and rerouting spam to a spam folder. Reinforcement learning uses ML to train models to identify and respond to cyberattacks and detect intrusions.

Machine Learning 51

Machine Learning Marketing Forecasting Modeling 51

IBM Big Data Hub

AUGUST 28, 2023

Text classification: Useful for tasks like sentiment classification, spam filtering and topic classification, text classification involves categorizing documents into predefined classes or categories. And with advanced software like IBM Watson Assistant , social media data is more powerful than ever.

Data mining 57

Data mining Machine Learning Deep Learning Marketing 57

Insight

MARCH 25, 2020

In this blog, I will share how I built Pair , a scalable web application that takes in a product image, analyzes its design features using convolutional neural network, and recommends products in other categories with similar style elements. In this project, I was curious to see if deep learning approaches?—?specifically

Deep Learning 53

Deep Learning Metrics Data Processing Interactive 53

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? Meta-Learning v.s.

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

Ontotext

MAY 22, 2023

When sales performance is analyzed and correlated with marketing data, for instance, it is critical to make sure that across the board there is good alignment regarding the categories of products, the regions, the suppliers and the relationships between them. We use other deep learning techniques for such tasks.

Management 59

Management Unstructured Data Metadata Cost-Benefit 59

Cloudera

NOVEMBER 15, 2021

This blog discusses quantifications, types, and implications of data. Deep Learning, a subset of AI algorithms, typically requires large amounts of human annotated data to be useful. The post The Rise of Unstructured Data appeared first on Cloudera Blog. The word “data” is ubiquitous in narratives of the modern world.

Unstructured Data 86

Unstructured Data Recreation/Entertainment Structured Data Reporting 86

CDW Research Hub

NOVEMBER 12, 2020

CPUs are sufficient for basic AI workloads, but GPUs are more ideally suited for deep learning workloads, which can require multiple large datasets and scalable neural networks. Perhaps one of the biggest issues that falls into this category is not having a full understanding of the scalability requirements.

ROI 79

ROI Deep Learning Machine Learning Business Objectives 79

DataRobot Blog

FEBRUARY 14, 2022

Model explainability is typically grouped into the categories of Global Explainability and Local Explainability. Because our dataset contains image data, DataRobot used models that contain deep learning based image featurizers. Local Explainability tells you why the model made a certain prediction for an individual row.

Machine Learning 52

Machine Learning Metrics Sales Modeling 52

Insight

MARCH 28, 2019

These categories are relatively broad (e.g. To measure this sentiment, Derek classified each sentence in a review as belonging to one of five categories: Culture & Values, Work/Life Balance, Senior Management, Compensation & Benefits, Career Opportunities (the same five dimensions Glassdoor asks employees to rate along).

Dashboards 70

Dashboards Visualization Data Science Machine Learning 70

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.

Metrics 58

Metrics Optimization Modeling Testing 58

Ontotext

FEBRUARY 26, 2024

Taxonomies, with their hierarchical structure, enable the identification of broader categories or themes present within the content. The semantic model leverages ontologies and taxonomies.

Data-driven 59

Data-driven Risk Modeling Risk Management 59

Insight

MARCH 4, 2020

In this blog, I’ll describe how to solve this with Contextual Topic Identification, leveraging machine learning methods to identify semantically similar groups and surface relevant category tags of the reviews. Steam review screenshot Dataset I used the Steam Review Dataset , published on Kaggle as a .csv csv file.

Visualization 81

Visualization Modeling Metrics Measurement 81

Cloudera

SEPTEMBER 12, 2018

Across well over 100 nominations, this year’s 11 winners, representing nine categories and six industries, demonstrate outstanding innovation to accelerate their mission, technical advancement, and overall impact. Congratulations to the Sixth Annual Data Impact Awards winners appeared first on Cloudera Blog.

Machine Learning 45

Machine Learning Big Data Data Science Data Warehouse 45

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.

Manufacturing 75

Manufacturing Machine Learning Testing Software 75

IBM Big Data Hub

DECEMBER 20, 2023

Machine learning types Machine learning algorithms fall into five broad categories: supervised learning, unsupervised learning, semi-supervised learning, self-supervised and reinforcement learning. AI studio The post Five machine learning types to know appeared first on IBM Blog.

Machine Learning 81

Machine Learning Modeling Deep Learning Predictive Modeling 81

IBM Big Data Hub

JANUARY 10, 2024

Data: AI systems learn and make decisions based on data, and they require large quantities of data to train effectively, especially in the case of machine learning (ML) models. text, images, audio and more), which enables the system to generalize its learning to new, unseen data.

Unstructured Data 82

Unstructured Data Deep Learning Cost-Benefit Modeling 82

IBM Big Data Hub

OCTOBER 12, 2023

Unlike basic machine learning models, deep learning models allow AI applications to learn how to perform new tasks that need human intelligence, engage in new behaviors and make decisions without human intervention. The three kinds of AI based on capabilities 1. Explore watsonx.ai

Machine Learning 73

Machine Learning Deep Learning Interactive Modeling 73

IBM Big Data Hub

NOVEMBER 14, 2023

Figure 1: The Data Monetization Lifecycle The raw data that fuels data monetization will come from three source categories: enterprise systems, external data and personal data. Realize : Each data service has an agreed unit of value which is transacted and measured.

Strategy 112

Strategy Cost-Benefit Data-driven Measurement 112

BizAcuity

MARCH 31, 2022

By 2025, AI will be the top category driving infrastructure decisions, due to the maturation of the AI market, resulting in a tenfold growth in compute requirements. For accurate predictions, companies now use various data models, machine and deep learning techniques to continuously improve and refine the quality of the outcome.

Data Analytics 52

Data Analytics Analytics Prescriptive Analytics Predictive Analytics 52

DataRobot Blog

MARCH 9, 2022

With that said, recent advances in deep learning methods have allowed models to improve to a point that is quickly approaching human precision on this difficult task. LSTMs and other recurrent neural networks RNNs are probably the most commonly used deep learning models for NLP and with good reason. More advanced models.

Machine Learning 52

Machine Learning Deep Learning Modeling Measurement 52

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. Know the limitations of your existing dataset and answer these questions: What categories of data are there? Datasets have quickly grown too huge, complex, and fast-moving for humans to grapple with.

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

Smart Data Collective

OCTOBER 14, 2019

Ludwig is a tool that allows people to build data-based deep learning models to make predictions. When Google talked about releasing this tool in its blog, the brand pointed out that if you don’t protect user data, you risk losing people’s trust. The website breaks down the types of charts into categories.

Data Science 67

Data Science Machine Learning Big Data Interactive 67

Domino Data Lab

JULY 22, 2019

This blog post provides a concise session summary, a video, and a written transcript. and drop your deep learning model resource footprint by 5-6 orders of magnitude and run it on devices that don’t even have batteries. Paco Nathan presented, “Data Science, Past & Future” , at Rev. Session Summary.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109