Rocket-Powered Data Science

DECEMBER 15, 2018

If we cannot know that ( i.e., because it truly is unsupervised learning), then we would like to know at least that our final model is optimal (in some way) in explaining the data. In those intermediate steps it serves as an evaluation (or validation) metric. We started this article mentioning Confucius and his wisdom.

Machine Learning 171

Machine Learning Optimization Metrics Modeling 171

Sisense

APRIL 10, 2020

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. There are many performance metrics to evaluate performance of Machine Learning models. R libraries.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

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

Cloudera

SEPTEMBER 20, 2022

The state of the art in AI systems for artistic tasks almost universally use deep-learning models, which presuppose a significant amount of compute resources both to create them, and once created to continue to use them for producing images. Access — who can use it? Data — where does it come from? Misuse — what could go wrong?

Deep Learning 87

Deep Learning Cost-Benefit Risk Data-driven 87

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 56

Machine Learning Data Science Statistics Deep Learning 56

Sisense

AUGUST 6, 2020

In this article, we’ll dig into the ways AI can help you accomplish these goals, allowing you and your team to envision the future of your product or service. An obvious mechanical answer is: use relevance as a metric. Another important method is to benchmark existing metrics. Improving performance with AI.

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

Domino Data Lab

SEPTEMBER 9, 2019

This article provides a brief introduction to natural language using spaCy and related libraries in Python. This article and paired Domino project provide a brief introduction to working with natural language (sometimes called “text analytics”) in Python using spaCy and related libraries. …. deep learning on edge devices.

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110

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 187

Machine Learning Modeling Testing Risk Management 187

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. What metrics are used to evaluate success?

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Sisense

MAY 26, 2020

For more details, refer to this article. Word embedding is a type of word representation that allows words with similar meanings to be understood by machine learning algorithms. For more details, check out this article. It also involves converting all text to lowercase. Data standardization and lemmatization.

Unstructured Data 98

Unstructured Data Big Data Testing Machine Learning 98

Insight

MARCH 25, 2020

After reading this, I hope you can learn how to build deep learning models using TensorFlow Keras, productionalize the model as a Streamlit app, and deploy it as a Docker container on Google Cloud Platform (GCP) using Google Kubernetes Engines (GKE). In this project, I was curious to see if deep learning approaches?—?specifically

Deep Learning 53

Deep Learning Metrics Data Processing Interactive 53

Cloudera

FEBRUARY 16, 2022

In this article, we explore model governance, a function of ML Operations (MLOps). We will learn what it is, why it is important and how Cloudera Machine Learning (CML) is helping organisations tackle this challenge as part of the broader objective of achieving Ethical AI. The complete list is shown below: Model Lineage .

Machine Learning 83

Machine Learning Modeling Metadata Recreation/Entertainment 83

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. 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.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

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. It just looks like plausible English.

Management 110

Management Machine Learning Experimentation Metrics 110

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 Modeling Deep Learning Metrics 111

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. deep learning) there is no guaranteed explainability. from sklearn import metrics. from datetime import datetime.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

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

FEBRUARY 10, 2019

Metrics can also increase a lot in the early days of a project and then suddenly hit a wall. Humans likely not even notice the difference but modern deep learning networks suffered a lot. Microsoft hadn’t predicted how quickly their Tay bot would learn bad behavior from trolls on Twitter. What’s Next?‍

Machine Learning 63

Machine Learning Modeling Deep Learning Metrics 63

DataRobot Blog

DECEMBER 20, 2022

In this article, we’ll showcase the ability of AI to improve the quality of the potential investment’s future performance, with a specific example from the real estate segment. In this article, we’ll first take a closer look at the concept of Real Estate Data Intelligence and the potential of AI to become a game changer in this niche.

Management 59

Management Machine Learning Optimization Modeling 59

O'Reilly on Data

JANUARY 25, 2021

We don’t see that in our data, though there are certainly some metrics to say that artificial intelligence has stalled. A backlash is only to be expected when deep learning applications are used to justify arresting the wrong people , and when some police departments are comfortable using software with a 98% false positive rate.

Machine Learning 362

Machine Learning Software Testing Technology 362

DataRobot Blog

FEBRUARY 2, 2023

The creation of foundation models is one of the key developments in the field of large language models that is creating a lot of excitement and interest amongst data scientists and machine learning engineers. These models are trained on massive amounts of text data using deep learning algorithms.

Modeling 52

Modeling Machine Learning Data Processing Interactive 52

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. Introduction. Welcome back to our monthly burst of themes and conferences.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

JULY 22, 2019

Greg Linden ‘s article about splitting the website on Amazon. 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. We have an article on this on Domino. By that point, they had all reached the same conclusion.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109