Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. In this article, we explore model governance, a function of ML Operations (MLOps). Machine Learning Model Lineage. Machine Learning Model Visibility . Machine Learning Model Explainability .

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

CIO Business Intelligence

SEPTEMBER 29, 2023

Most relevant roles for making use of NLP include data scientist , machine learning engineer, software engineer, data analyst , and software developer. TensorFlow Developed by Google as an open-source machine learning framework, TensorFlow is most used to build and train machine learning models and neural networks.

Deep Learning 143

Deep Learning Machine Learning Modeling Consulting 143

Analytics Vidhya

DECEMBER 26, 2023

In this article, we will explore the origins, background, and implications of this experiment, as well as its relationship with the Symbol Grounding Problem and the Turing Test. We […] The post Exploring the Intricacies of the Chinese Room Experiment in AI appeared first on Analytics Vidhya.

Testing 304

Testing Analytics IT Deep Learning 304

DataRobot

FEBRUARY 10, 2021

Machine learning is a glass cannon. The promise and power of AI lead many researchers to gloss over the ways in which things can go wrong when building and operationalizing machine learning models. As a data scientist, one of my passions is to reproduce research papers as a learning exercise.

Modeling 98

Modeling Deep Learning Machine Learning Testing 98

O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. In this article, we turn our attention to the process itself: how do you bring a product to market? Identifying the problem. Agreeing on metrics.

Marketing 362

Marketing Experimentation Metrics Testing 362

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

CIO Business Intelligence

JUNE 1, 2022

These supercomputers power exciting innovations in deep learning, disease control, and physics—think bionic eyes, DNA sequencing for infectious disease research, and the study of time crystals. . CSIRO’s Bracewell Delivers Deep Learning, Bionic Vision. Bracewell’s IO500 score was 99.64, IO500 BW 39.90

Deep Learning 90

Deep Learning Snapshot Optimization Data Quality 90

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 127

Enterprise Modeling Testing Data-driven 127

Smart Data Collective

FEBRUARY 20, 2022

In this article we will list 10 things AWS can do for your SaaS company. This article finally gets to the core question we started with: what can AWS do for your SaaS business? AWS also offers developers the technology to develop smart apps using machine learning and complex algorithms. Let’s just dive right in.

Cost-Benefit 115

Cost-Benefit Data Lake Software Machine Learning 115

IBM Big Data Hub

JULY 6, 2023

To keep up with the pace of consumer expectations, companies are relying more heavily on machine learning algorithms to make things easier. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other? Machine learning is a subset of AI.

Deep Learning 63

Deep Learning Machine Learning Unstructured Data Modeling 63

Grooper

JUNE 22, 2021

This is how machine learning works. This is the reality of deep learning, or convoluted neural networks. In the case of machine learning, very specific outcomes are achievable. There are no surprises with machine learning. Real World Deep Learning / Neural Net Example.

Deep Learning 57

Deep Learning Machine Learning Software Testing 57

Smarten

JUNE 14, 2023

In this article, we will discuss GPT, its roots and its limitations, in order to provide insight into where this technology is today and how it can be used now to inform and support apps, software products and technology services. GPT, or Generative Pre-Trained Transformer, is a Large Language Model (LLM).

Optimization 52

Optimization IT Digital Transformation Consulting 52

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 ). Functionality comparison cheat sheet.

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110

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.

Modeling 77

Modeling Machine Learning Deep Learning Testing 77

Domino Data Lab

JULY 28, 2021

In this article, we will provide an overview of the three overlapping components of data science, the importance of communication and collaboration, and how the Domino Data Lab MLOps platform can help improve the speed and efficiency of your team. After cleaning, the data is now ready for processing.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

CIO Business Intelligence

APRIL 24, 2023

How we got here The most notable enabling technologies in generative AI are deep learning, embeddings, transfer learning (all of which emerged in the early to mid-2000s), and neural net transformers (invented in 2017). Deep learning emerged in academia in the early 2000s, with broader industry adoption starting around 2010.

Enterprise 52

Enterprise Deep Learning Modeling Machine Learning 52

Smart Data Collective

JULY 30, 2020

In December, Springer published an insightful article about the value of deep learning for VPNs. The article “Deep learning-based real-time VPN encrypted traffic identification methods” delves into the use of machine learning to improve encryption models. What is VPN.

Machine Learning 86

Machine Learning Testing Deep Learning Advertising 86

Domino Data Lab

JULY 28, 2021

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. The most commonly used techniques today are under the umbrella of machine learning.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

IBM Big Data Hub

APRIL 18, 2024

While AGI promises machine autonomy far beyond gen AI, even the most advanced systems still require human expertise to function effectively. Building an in-house team with AI, deep learning , machine learning (ML) and data science skills is a strategic move. What are the types of AGI?

Cost-Benefit 97

Cost-Benefit Modeling Manufacturing Interactive 97

CDW Research Hub

JULY 31, 2019

Recent advances in machine learning, and more specifically its subset, deep learning, have made it possible for computers to better understand natural language. For the purposes of this article, we will use the Rasa, an open source stack that provides tools to build contextual AI assistants. What’s next?

Deep Learning 49

Deep Learning Machine Learning Testing Modeling 49

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. How Deep Learning scales based on the amount of Data [Copyright: Andrew Ng ]. Transfer Learning?—?YOLO. Precision?—?Recall

Deep Learning 84

Deep Learning Machine Learning Metadata Data Science 84

Domino Data Lab

APRIL 3, 2019

This month’s article features updates from one of the early data conferences of the year, Strata Data Conference – which was held just last week in San Francisco. In particular, here’s my Strata SF talk “Overview of Data Governance” presented in article form. Welcome back to our monthly burst of themes and conferences.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Smart Data Collective

FEBRUARY 24, 2021

However, this ever-evolving machine learning technology might surprise you in this regard. The truth is that machine learning is now capable of writing amazing content. Machine Learning to Write your College Essays. Machine Learning to Write your College Essays.

Machine Learning 91

Machine Learning Deep Learning Strategy Reporting 91

DataRobot Blog

MARCH 9, 2022

This article was originally published at Algorithimia’s website. This article may not be entirely up-to-date or refer to products and offerings no longer in existence. 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.

Machine Learning 52

Machine Learning Deep Learning Modeling Measurement 52

Smart Data Collective

SEPTEMBER 9, 2019

The majority of machine learning and deep learning solutions have focused on fundamental analysis of securities. However, deep learning and other artificial intelligence technologies will also change the future of technical analysis as well. New developments in deep learning with technical analysis.

Deep Learning 66

Deep Learning Forecasting Machine Learning Strategy 66

O'Reilly on Data

OCTOBER 19, 2021

Much has been written about struggles of deploying machine learning projects to production. This approach has worked well for software development, so it is reasonable to assume that it could address struggles related to deploying machine learning in production too. An Overarching Concern: Correctness and Testing.

IT 346

IT Testing Experimentation Software 346

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

APRIL 15, 2022

And 20% of IT leaders say machine learning/artificial intelligence will drive the most IT investment. Insights gained from analytics and actions driven by machine learning algorithms can give organizations a competitive advantage, but mistakes can be costly in terms of reputation, revenue, or even lives.

Analytics 144

Analytics Machine Learning Insurance Testing 144

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 57

Analytics Predictive Modeling KPI Machine Learning 57

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. Check out this great Medium article that discusses the difference between tensors and multidimensional arrays. Check out the Keras documentation here.

Metrics 58

Metrics Optimization Modeling Testing 58

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Be aware that machine learning often involves working on something that isn’t guaranteed to work.

Management 110

Management Machine Learning Experimentation Metrics 110

O'Reilly on Data

JUNE 7, 2022

We have a lot of vague notions about the Turing test, but in the final analysis, Turing wasn’t offering a definition of machine intelligence; he was probing the question of what human intelligence means. Similarly, if I want a system that writes news articles about religious institutions, do I want a fully general intelligence?

Modeling 363

Modeling Interactive Optimization Deep Learning 363

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. Machine learning model interpretability. Introduction.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

IBM Big Data Hub

JANUARY 10, 2024

In this article, we’ll discuss how AI technology functions and lay out the advantages and disadvantages of artificial intelligence as they compare to traditional computing methods. text, images, audio and more), which enables the system to generalize its learning to new, unseen data.

Unstructured Data 84

Unstructured Data Deep Learning Cost-Benefit Modeling 84

Sisense

MAY 26, 2020

For more details, refer to this article. While training a model for NLP, words not present in the training data commonly appear in the test data. Because of this, predictions made using test data may not be correct. To solve this problem, machines need to capture the semantic meaning of words. can’t” becomes “can not”).

Unstructured Data 98

Unstructured Data Big Data Testing Machine Learning 98

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. So what are the high-level steps to incorporate AI and machine learning into new and existing products? Be sure test cases represent the diversity of app users. Improving performance with AI.

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

BizAcuity

MARCH 31, 2022

There have been so many articles published about AI and its applications, you can find millions of articles from broad concepts to deep technical literature on the internet. This article (like thousands of other articles), is aimed at presenting consolidated information about AI for business in simple language.

Data Analytics 52

Data Analytics Analytics Prescriptive Analytics Predictive Analytics 52