KDnuggets

DECEMBER 11, 2019

AI, Analytics, Machine Learning, Data Science, Deep Learning Research Main Developments and Key Trends; Down with technical debt! the most relevant Metrics in a Nutshell. Clean #Python for #DataScientists; Calculate Similarity?-?the

Deep Learning 47

Deep Learning Machine Learning Data Science Metrics 47

CIO Business Intelligence

JANUARY 24, 2024

It’s the culmination of a decade of work on deep learning AI. Deep learning AI: A rising workhorse Deep learning AI uses the same neural network architecture as generative AI, but can’t understand context, write poems or create drawings. You probably know that ChatGPT wasn’t built overnight.

Deep Learning 87

Deep Learning Unstructured Data Machine Learning Modeling 87

DataRobot Blog

FEBRUARY 14, 2022

Because our dataset contains image data, DataRobot used models that contain deep learning based image featurizers. Typically this means finding out how many predictions have been made, how many requests have been made to the deployment, and other performance-related metrics. Learn More About Explainable AI. Learn more.

Machine Learning 52

Machine Learning Metrics Sales Modeling 52

KDnuggets

OCTOBER 16, 2019

This week on KDnuggets: Beyond Word Embedding: Key Ideas in Document Embedding; The problem with metrics is a big problem for AI; Activation maps for deep learning models in a few lines of code; There is No Such Thing as a Free Lunch; 8 Paths to Getting a Machine Learning Job Interview; and much, much more.

Metrics 52

Metrics Deep Learning Machine Learning Modeling 52

O'Reilly on Data

JULY 28, 2020

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 363

Marketing Experimentation Metrics Testing 363

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

Management 339

Management Machine Learning Experimentation Metrics 339

Domino Data Lab

FEBRUARY 10, 2019

This blog post provides insights into why machine learning teams have challenges with managing machine learning projects. Why are Machine Learning Projects so Hard to Manage? I’ve watched lots of companies attempt to deploy machine learning?—?some Why is this?

Machine Learning 63

Machine Learning Modeling Deep Learning Metrics 63

Analytics Vidhya

MARCH 7, 2024

While ChatGPT, developed by OpenAI, stands as a titan in conversational AI, “Perplexity” pertains more to a performance metric used in evaluating language models. Introduction In artificial intelligence, particularly in natural language processing, two terms often come up: Perplexity and ChatGPT.

Metrics 266

Metrics Modeling Analytics Deep Learning 266

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

IBM Big Data Hub

NOVEMBER 29, 2023

With the emergence of new advances and applications in machine learning models and artificial intelligence, including generative AI, generative adversarial networks, computer vision and transformers, many businesses are seeking to address their most pressing real-world data challenges using both types of synthetic data: structured and unstructured.

Metrics 80

Metrics Machine Learning Statistics Risk 80

datapine

MAY 14, 2019

2) “Deep Learning” by Ian Goodfellow, Yoshua Bengio and Aaron Courville. Best for: This best data science book is especially effective for those looking to enter the data-driven machine learning and deep learning avenues of the field. 4) “Machine Learning Yearning” by Andrew Ng.

Data Science 350

Data Science Machine Learning Data-driven Big Data 350

Rocket-Powered Data Science

DECEMBER 15, 2018

This wisdom applies not only to life but to machine learning also. Specifically, the availability and application of labeled data (things past) for the labeling of previously unseen data (things future) is fundamental to supervised machine learning. A related problem also arises in unsupervised machine learning.

Machine Learning 171

Machine Learning Optimization Metrics Modeling 171

Smart Data Collective

JULY 1, 2023

Image annotation is the act of labeling images for AI and machine learning models. The resulting structured data is then used to train a machine learning algorithm. There are a lot of image annotation techniques that can make the process more efficient with deep learning.

Metrics 64

Metrics Machine Learning Uncertainty Deep Learning 64

Cloudera

FEBRUARY 25, 2021

Cloudera has a front-row seat to organizational challenges as those enterprises make Machine Learning a core part of their strategies and businesses. The work of a machine learning model developer is highly complex. Here’s a preview of what you can leverage with one click in CML: Deep Learning for Anomaly Detection.

Deep Learning 84

Deep Learning Machine Learning Forecasting Visualization 84

Corinium

JUNE 6, 2019

A playbook for this is to run multiple experiments in parallel and create ‘MVPs’ (fail/learn fast), as well as incorporate feedback mechanisms to enable an improvement loop, and scaling the ones that show the fastest path to ROI. Build multiple MVPs to test conceptually and learn from early user feedback.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

DataRobot

DECEMBER 20, 2021

Image recognition is one of the most relevant areas of machine learning. Deep learning makes the process efficient. However, not everyone has deep learning skills or budget resources to spend on GPUs before demonstrating any value to the business. With frameworks like Tensorflow , Keras , Pytorch, etc.,

Visualization 97

Visualization Deep Learning Machine Learning Modeling 97

IBM Big Data Hub

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. What is MLOps?

Data Science 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

Sisense

APRIL 10, 2020

These support a wide array of uses, such as data analysis, manipulation, visualizations, and machine learning (ML) modeling. When we say “modeling” in data science, we mean teaching a program to learn from training data using machine learning algorithms. Again, the library you use will be based on your use case.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

CIO Business Intelligence

JUNE 21, 2022

In addition to quantitative ROI metrics, HPC research was also shown to save lives, lead to important public/private partnerships, and spur innovations. . Real-time big data analytics, deep learning, and modeling and simulation are newer uses of HPC that governments are embracing for a variety of applications. HPC Growth in U.S.

Deep Learning 96

Deep Learning Big Data ROI Software 96

DataRobot

OCTOBER 20, 2021

In order to scale responsible AI, organizations should implement these fundamental building blocks of data literacy: The data science and machine learning workflow: Learning about the steps required to create predictions from raw data helps stakeholders develop an understanding of AI project implementation.

Machine Learning 52

Machine Learning Metrics Risk Deep Learning 52

Cloudera

OCTOBER 5, 2020

Cloudera announced today a new collaboration with NVIDIA that will help Cloudera customers accelerate data engineering, analytics, machine learning and deep learning performance with the power of NVIDIA GPU computing across public and private clouds. CDP enables enterprise customers to leverage Apache Spark 3.0

Enterprise 93

Enterprise Machine Learning Deep Learning Software 93

DataRobot Blog

MAY 26, 2022

Validating Modern Machine Learning (ML) Methods Prior to Productionization. Validating Machine Learning Models. Figure 4: DataRobot provides an interactive ROC curve specifying relevant model performance metrics on the bottom right.

Risk 52

Risk Modeling Metrics Business Objectives 52

CIO Business Intelligence

JUNE 1, 2022

and artificial intelligence (AI) and machine learning (ML) technologies. . Aside from monitoring components over time, sensors also capture aerodynamics, tire pressure, handling in different types of terrain, and many other metrics. which are virtual models of objects, systems, or processes ? Just starting out with analytics?

Manufacturing 102

Manufacturing Digital Transformation Predictive Analytics Strategy 102

IBM Big Data Hub

SEPTEMBER 6, 2023

AI marketing is the process of using AI capabilities like data collection, data-driven analysis, natural language processing (NLP) and machine learning (ML) to deliver customer insights and automate critical marketing decisions. What is AI marketing?

Marketing 78

Marketing Technology Advertising ROI 78

IBM Big Data Hub

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data.

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

IBM Big Data Hub

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. What is machine learning?

Machine Learning 74

Machine Learning Modeling Deep Learning Predictive Modeling 74

CIO Business Intelligence

JANUARY 4, 2023

Moving forward, we will see workflows that are more capable and widely adopted to facilitate edge-core-cloud needs like generating meshes, performing 3D simulations, performing post-simulation data analysis, and feeding data into machine learning models—which support, guide, and in some case replace the need for simulation.

Cost-Benefit 59

Cost-Benefit Machine Learning Modeling Uncertainty 59

DataRobot Blog

JULY 27, 2022

However, machine learning utilization is often unpredictable, which makes scaling sometimes a huge challenge. Investing in AI/ML is no longer an option but is critical for organizations to remain competitive. Many engineering teams don’t pay the necessary attention to it. Data Science Expertise Meets Scalability.

Metrics 52

Metrics Deep Learning Modeling Machine Learning 52

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. Introduction Machine learning models often behave unpredictably, as data scientists would be the first to tell you. A more general approach is to learn a Generalized Additive Model (GAM).

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Domino Data Lab

JULY 28, 2021

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. 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

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 88

Deep Learning Cost-Benefit Risk Data-driven 88

Smart Data Collective

FEBRUARY 21, 2021

Deep learning tech is influencing and enhancing many industries, promising to provide insights into key business operations which were not previously possible to unearth. One of the biggest applications of this technology lies with using deep learning to streamline fleet management. Route adjustments made in real time.

Deep Learning 97

Deep Learning Management Cost-Benefit Manufacturing 97

DataRobot Blog

APRIL 22, 2022

In our previous post , we talked about how red AI means adding computational power to “buy” more accurate models in machine learning , and especially in deep learning. This view actually delivers four out of the five efficiency metrics that we discussed in the previous blog post.

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

IBM Big Data Hub

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. Prioritizing tracking metrics accurately measures the success of your implementation. ML algorithms understand language in the NLU subprocesses and generate human language within the NLG subprocesses.

Enterprise 95

Enterprise Interactive Data-driven Key Performance Indicator 95

Domino Data Lab

JULY 22, 2019

why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”. But for most enterprise, using machine learning…not really.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

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

Edwin Chen

OCTOBER 6, 2014

Click-through rate may be your initial hope…but after a bit of thought, it's not clear that it's the best metric after all. Metrics like CTR, or even number of favorites and retweets, will probably optimize for showing quick one-liners and pictures of funny cats. Amazon, and Moving Beyond Log-Based Metrics.

Metrics 79

Metrics Measurement Machine Learning Testing 79