O'Reilly on Data

JUNE 18, 2019

Moreover, the domain knowledge, which often is not encoded in the data (nor fully documented), is an integral part of this data (see this article from Forbes). In this post, we shed some light on various efforts toward generating data for machine learning (ML) models. See this article on data integration status for details.

Machine Learning 279

Machine Learning Data Quality Statistics Modeling 279

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

Smart Data Collective

JUNE 4, 2021

Data science needs knowledge from a variety of fields including statistics, mathematics, programming, and transforming data. Mathematics, statistics, and programming are pillars of data science. In data science, use linear algebra for understanding the statistical graphs. It is the building block of statistics.

Data Science 133

Data Science Statistics Deep Learning Machine Learning 133

O'Reilly on Data

JULY 28, 2020

In this article, we turn our attention to the process itself: how do you bring a product to market? Products based on deep learning can be difficult (or even impossible) to develop; it’s a classic “high return versus high risk” situation, in which it is inherently difficult to calculate return on investment.

Marketing 364

Marketing Experimentation Metrics Testing 364

Analytics Vidhya

MAY 23, 2023

But have you ever wondered what it takes to become an artificial intelligence engineer This article will equip you with the essential information to take the first steps […] The post How to Become an AI Engineer in 2023? AI is revolutionizing industries and transforming our daily lives, from self-driving cars to virtual assistants.

Analytics 319

Analytics Deep Learning IT Machine Learning 319

Analytics Vidhya

FEBRUARY 4, 2022

Overview Motivation to Learn R Covering the BASICS & MUST KNOW Concepts in R Introduction Since you are reading this article, I am assuming that right now you are in your journey of becoming a data scientist. There is a high possibility that you already are aware of some of the data visualization and analytics […].

Visualization 289

Visualization Analytics Deep Learning Statistics 289

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

O'Reilly on Data

FEBRUARY 18, 2020

Usage specific to Python as a programming language grew by just 4% in 2019; by contrast, usage that had to do with Python and ML—be it in the context of AI, deep learning, and natural language processing, or in combination with any of several popular ML/AI frameworks—grew by 9%. We’re appropriating them differently.

Data-driven 363

Data-driven Software Statistics Marketing 363

Analytics Vidhya

APRIL 11, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon. Introduction This article is an introduction to autonomous navigation. First, The post Introduction to Autonomous Navigation – LIDAR, Sensor Fusion, Kalman Filter appeared first on Analytics Vidhya.

Data Science 235

Data Science Publishing Analytics Deep Learning 235

O'Reilly on Data

DECEMBER 12, 2019

This article is meant to be a short, relatively technical primer on what model debugging is, what you should know about it, and the basics of how to debug models in practice. If you’re using Python and deep learning libraries, the CleverHans and Foolbox packages can also help you debug models and find adversarial examples.

Machine Learning 275

Machine Learning Modeling Testing Risk Management 275

Domino Data Lab

JULY 28, 2021

Through a marriage of traditional statistics with fast-paced, code-first computer science doctrine and business acumen, data science teams can solve problems with more accuracy and precision than ever before, especially when combined with soft skills in creativity and communication. Math and Statistics Expertise.

Data Science 110

Data Science Statistics Predictive Analytics Recreation/Entertainment 110

CIO Business Intelligence

AUGUST 11, 2023

Lilly Translate uses NLP and deep learning language models trained with life sciences and Lilly content to provide real-time translation of Word, Excel, PowerPoint, and text for users and systems. hours of on-demand video, two articles, and three downloadable resources. NLTK is offered under the Apache 2.0 It consists of 11.5

Unstructured Data 142

Unstructured Data Machine Learning Data Science Data mining 142

O'Reilly on Data

MARCH 20, 2019

Watermarking is a term borrowed from the deep learning security literature that often refers to putting special pixels into an image to trigger a desired outcome from your model. A lot of the contemporary academic machine learning security literature focuses on adaptive learning, deep learning, and encryption.

Modeling 272

Modeling Machine Learning Predictive Modeling Consulting 272

Smart Data Collective

NOVEMBER 1, 2020

With that being said, let’s have a closer look at how unsupervised machine learning is omnipresent in all industries. What Is Unsupervised Machine Learning? If you’ve ever come across deep learning, you might have heard about two methods to teach machines: supervised and unsupervised. We have, and it’s a hell of a task.

Machine Learning 145

Machine Learning Data mining Visualization Statistics 145

Sisense

APRIL 10, 2020

R is a tool built by statisticians mainly for mathematics, statistics, research, and data analysis. We’ll actually do this later in this article. These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling. R: Analytics powerhouse. R libraries.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Smart Data Collective

MAY 8, 2022

Brands are closely working to solve this as they dive deep into the world of big data analytics. Well, don’t go anywhere because, in this article, we will show you how you can use big data analytics combined with AI to achieve the best performance possible. What is the relationship between big data analytics and AI?

Big Data 122

Big Data Data Analytics Analytics Statistics 122

Data Science and Beyond

DECEMBER 23, 2018

In my opinion it’s more exciting and relevant to everyday life than more hyped data science areas like deep learning. However, I’ve found it hard to apply what I’ve learned about causal inference to my work. I’ve been interested in the area of causal inference in the past few years.

Deep Learning 103

Deep Learning Experimentation Data Science Data-driven 103

Smart Data Collective

SEPTEMBER 25, 2023

Have you ever wondered what it would be like if machines could learn to speak every language in the world? In this article, how does AI translation work ? With the help of neural networks, machines are now able to learn and speak multiple languages, bridging the language barrier that once hindered effective communication.

Deep Learning 124

Deep Learning Statistics Machine Learning Technology 124

Rocket-Powered Data Science

JULY 6, 2023

For a short introduction to generative AI, see my article “ Generative AI – Chapter 1, Page 1 ”. The AI conversations, especially in technical circles, have focused intensively on generative AI, the creation of written content, images, videos, marketing copy, software code, speeches, and countless other things.

Strategy 153

Strategy Enterprise Software Marketing 153

Domino Data Lab

DECEMBER 17, 2019

This article provides concise insights into GANs to help data scientists and researchers assess whether to investigate GANs further. Logistic regression, from a statistical perspective, is an example of a discriminative approach. GANs: Potentially useful for semisupervised learning and multi-model settings. Introduction.

Deep Learning 64

Deep Learning Machine Learning Modeling Statistics 64

Domino Data Lab

DECEMBER 1, 2019

This article covers how to detect data drift for models that ingest image data as their input in order to prevent their silent degradation in production. In the context of machine learning, we consider data drift 1 to be the change in model input data that leads to a degradation of model performance. Detecting image drift. References.

Machine Learning 77

Machine Learning Modeling Deep Learning Testing 77

Data Science and Beyond

OCTOBER 15, 2017

Here are my thoughts from 2014 on defining data science as the intersection of software engineering and statistics , and a more recent post on defining data science in 2018. I’ve also dabbled in deep learning , marine surveys , causality , and other things that I haven’t had the chance to write about.

Data Science 71

Data Science Deep Learning Machine Learning Data-driven 71

FineReport

DECEMBER 19, 2019

When it comes to data analysis, from database operations, data cleaning, data visualization , to machine learning, batch processing, script writing, model optimization, and deep learning, all these functions can be implemented with Python, and different libraries are provided for you to choose. From Google.

Knowledge Discovery 98

Knowledge Discovery Visualization Data mining Reporting 98

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.” Generative AI uses advanced machine learning algorithms and techniques to analyze patterns and build statistical models.

Enterprise 118

Enterprise Testing Modeling Data-driven 118

Sisense

APRIL 15, 2021

Augmented analytics (according to Gartner, which would know), uses technologies “such as machine learning [ML] and AI to assist with data preparation, insight generation, and insight explanation to augment how people explore and analyze data in analytics and BI platforms.”

Analytics 98

Analytics Data Science Machine Learning Modeling 98

IBM Big Data Hub

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

Ontotext

DECEMBER 15, 2023

It includes only ML papers and related entities; this SPARQL query shows some statistics: papers tasks models datasets methods evaluations repos 376557 4267 24598 8322 2101 52519 153476 We can start with these repositories (most of them are on Github) and get all their topics. We use Categories as a way of finding relevant articles.

Machine Learning 69

Machine Learning Deep Learning Interactive Statistics 69

Business Over Broadway

SEPTEMBER 5, 2018

Check out these links to get you started: UN Data from the United Nations Statistics Division. Using machine learning, deep learning, and visual recognition to improve critical processes. This article originally appeared on LinkedIn. If you understand the data, you understand the process that generates them.

Data Science 57

Data Science Statistics Advertising Deep Learning 57

Domino Data Lab

APRIL 21, 2021

In this article, we’ll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot anomalies that the human eye might not catch. In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Use of influence functions goes back to the 1970s in robust statistics.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Smarten

APRIL 19, 2024

In this article, we will discuss the current state of AI in analytics, as well as the future of this burgeoning industry and how it can be applied to analytics to simplify and clarify results and to make analytics easier for businesses and business users to leverage.

Analytics 59

Analytics Predictive Modeling KPI Machine Learning 59

Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. In this article, take a deep dive into data science and how Domino’s Enterprise MLOps platform allows you to scale data science in your business. In fact, deep learning was first described theoretically in 1943.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

Domino Data Lab

JULY 9, 2019

In 2001, Bill Cleveland writes this article saying, “You are doing it wrong.” This was one of several such articles, but that’s another talk. .” Then we can drill down and say what are the individual articles that over-index for that group or for that topic. That is an example of a descriptive tool. .”

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Domino Data Lab

MARCH 3, 2019

Seriously, this entire article merely skims the surface of those reports. Check the end of this article for key guidance synthesized from the practices of the leaders in the field. Ben and I also wrote articles for each of the surveys, summarizing the highlights. The data types used in deep learning are interesting.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

JULY 22, 2019

He was saying this doesn’t belong just in statistics. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. Greg Linden ‘s article about splitting the website on Amazon. We have an article on this on Domino. Tukey did this paper.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. Building an in-house team with AI, deep learning , machine learning (ML) and data science skills is a strategic move.

Cost-Benefit 89

Cost-Benefit Manufacturing Modeling Interactive 89

Domino Data Lab

JUNE 23, 2019

It used deep learning to build an automated question answering system and a knowledge base based on that information. But the reality is, if you give something, an arbitrary news article and ask it to do a generative summary, what comes out is often not factually correct and sometimes it’s not even sensible.

Management 110

Management Machine Learning Experimentation Metrics 110