machine learning frameworks

USAGE SUMMARY

The phrase "machine learning frameworks" is correct and usable in written English.
You can use it when discussing software tools or libraries that facilitate the development and implementation of machine learning models. Example: "TensorFlow and PyTorch are two of the most popular machine learning frameworks used by data scientists today."

✓ Grammatically correct

Academia

News & Media

Science

Human-verified examples from authoritative sources

Exact Expressions

40 human-written examples

Clipper sits in between frontend serving applications that query Clipper and machine learning frameworks that render predictions.

University of California, Berkeley

The talk proposes inductive approaches of bibliographers such as W.W. Greg and those creating machine learning frameworks are homologous.

University of California, Berkeley

Clipper simplifies deploying models from a wide range of machine learning frameworks by exposing a common REST interface and automatically ensuring low-latency and high-throughput predictions.

University of California, Berkeley

Furthermore, by introducing caching, batching, and adaptive model selection techniques, Clipper reduces prediction latency and improves prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks.

University of California, Berkeley

Interposed between end-user applications and a wide range of machine learning frameworks, Clipper introduces a modular architecture to simplify model deployment across frameworks.

University of California, Berkeley

Topics include probabilistic modeling of data, parameter constraints and model comparison, numerical methods including Markov Chain Monte Carlo, and connections to frequentist and machine learning frameworks.

Stanford University

Human-verified similar examples from authoritative sources

Similar Expressions

20 human-written examples

Ward, L., Agrawal, A., Choudhary, A. & Wolverton, C. A general-purpose machine learning framework for predicting properties of inorganic materials.

Nature

A machine learning framework is presented to assess post-earthquake structural safety.

Structural Safety

We initiate the study of incentives in a general machine learning framework.

Journal of Computer and System Sciences

Moradi, E., Pepe, A., Gaser, C., Huttunen, H. & Tohka, J. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects.

Nature

The machine learning framework is evaluated with SemEval-2012 and SemEval-2013 data from the spatial role labeling task.

Web Semantics: Science, Services and Agents on the World Wide Web

Expert writing Tips

Best practice

When discussing specific software, be precise. For example, instead of simply saying "machine learning frameworks", name the framework such as TensorFlow, PyTorch, or scikit-learn. This adds clarity and provides more context for your audience.

Common error

Avoid using "machine learning frameworks" as a catch-all term for all AI-related tools. Recognize the distinction between libraries, platforms, and complete frameworks to maintain precision in your writing.

Linguistic Context

The phrase "machine learning frameworks" functions primarily as a noun phrase. It identifies a category of software tools designed to facilitate the development, training, and deployment of machine learning models. As stated in Ludwig, the phrase is correct and usable in written English.

Expression frequency: Common

Frequent in

Academia

33%

News & Media

33%

Science

34%

Less common in

Formal & Business

0%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

In summary, "machine learning frameworks" is a grammatically sound and frequently used noun phrase that refers to software tools designed for machine learning development. According to Ludwig, the phrase is correct and usable in written English.

It is most commonly found in academic, scientific, and news contexts. When using this phrase, being specific about the framework (e.g., TensorFlow, PyTorch) enhances clarity. Ludwig AI underlines that you should be precise about frameworks. Related terms include "ML frameworks" and "AI development frameworks". Overall, understanding the nuances of this term is crucial for clear communication in the field of artificial intelligence and data science.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

ML frameworks

Abbreviated form, suitable for contexts where the audience is familiar with machine learning terminology.

AI development frameworks

Broader term encompassing frameworks used for artificial intelligence development, which may include machine learning.

Deep learning platforms

Specific to platforms designed for deep learning, a subfield of machine learning.

Machine learning tools

More general term referring to any tool used in machine learning, not necessarily a framework.

Predictive modeling frameworks

Focuses on frameworks used for building predictive models using machine learning.

Data science libraries

Libraries used in data science projects, which may include machine learning functionalities.

Computational intelligence platforms

A wider concept that covers machine learning, fuzzy logic, and evolutionary computation.

Artificial neural network tools

Specifically refers to tools for constructing and training artificial neural networks.

Statistical modeling software

Software used for creating statistical models, which may or may not involve machine learning.

Pattern recognition systems

Systems designed to recognize patterns in data, which can be implemented using machine learning.

FAQs

How to use "machine learning frameworks" in a sentence?

You can use "machine learning frameworks" to describe the software used to build and deploy machine learning models. For example, "TensorFlow and PyTorch are popular "machine learning frameworks"."

What's the difference between "machine learning frameworks" and libraries?

"Machine learning frameworks" provide a complete structure for building models, whereas libraries like NumPy offer specific tools that can be integrated into a larger system. Frameworks offer more structure, while libraries offer granular control.

Which is more appropriate, "AI frameworks" or "machine learning frameworks"?

"AI frameworks" is a broader term. If you're specifically referring to tools used for machine learning, then ""machine learning frameworks"" is more accurate. Otherwise you can use "AI frameworks".

What are some examples of "machine learning frameworks"?

Examples of ""machine learning frameworks"" include TensorFlow, PyTorch, scikit-learn, and Apache MXNet. These provide tools and structure for developing and deploying machine learning models.