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blog.paperspace.com article

Machine Learning Frameworks Comparison

https://blog.paperspace.com/which-ml-framework-should-i-use/

# Machine Learning Frameworks Comparison. In this post we compare popular machine learning frameworks like TensorFlow, Theano, Torch, Caffe, CNTK, MXnet, and more. When taking the deep-dive into Machine Learning (ML), choosing a framework can be daunting. TensorFlow was developed by the Google Brain Team for conducting research in machine learning and deep neural networks. Google recently moved away from Torch to TensorFlow which was a blow to other frameworks -- Torch and Theano in particular. Many describe TensorFlow as a more modern version of Theano after many important lessons about this new field/technology were learned over the years. Keras, a deep-learning library, was recently ported to run on TensorFlow which means any model written in Keras can now run on TensorFlow. Theano originated in 2007 at the University of Montreal at the widely renowned Institute for Learning Algorithms. As such, raw Theano is more of a research platform and ecosystem than a deep learning library. Microsoft Cognitive Toolkit, also known as CNTK, is Microsoft’s open-source deep-learning framework.

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repositorio-aberto.up.pt article

[PDF] Review of Machine Learning Deployment Frameworks

https://repositorio-aberto.up.pt/bitstream/10216/160794/2/681585.pdf

This dissertation focuses on evaluating and comparing several machine learning frame-works, specifically TensorFlow Lite, ONNXRuntime, and PyTorch, to identify the most suitable framework for efficient deployment on heterogeneous hardware platforms commonly used in IoT systems. iii Sustainable Development Goals (SDGs) This dissertation evaluates and compares machine learning frameworks like TensorFlow Lite, ON-NXRuntime, and PyTorch to identify the best framework for efficient deployment on heteroge-neous IoT hardware. iv Table 1: SDG Contributions of the Dissertation SDG Target How the target is met 9 9.5 The dissertation provides a systematic evaluation of best practices for deploy-ing machine learning models in resource-constrained environments, offering insights to improve the efficiency and scalability of AIoT solutions, contribut-ing to advanced industrial technologies. After a detailed evaluation of each framework, it’s concluded that TensorFlow Lite is the best overall choice for deploying ML models in IoT applications in the mobility sector. Future research should evaluate the potential of Glow in optimizing machine learning models for AIoT applications, specifically in the context of intelligent mobility and resource-constrained environments.

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logicstechnology.com news

A Comprehensive Comparison of the Best Machine Learning Frameworks for – Logics Technology Solutions Inc

https://logicstechnology.com/blogs/news/comparing-2024s-top-machine-learning-…

In this article, we’ll be **comparing 2024's top machine learning frameworks** to give small business owners insight into which options might best suit their needs. When it comes to **comparing 2024's top machine learning frameworks**, several key criteria can influence the decision-making process for small business owners in Canada. In the ever-evolving world of technology, **comparing 2024's top machine learning frameworks** is crucial for Canadian small business owners looking to harness the power of data-driven decision-making. As machine learning continues to transform industries, understanding the unique features and capabilities of leading frameworks such as TensorFlow, PyTorch, and Scikit-Learn can position your business for success. As the landscape of technology continues to evolve, Canadian small business owners are increasingly interested in understanding how to leverage advancements in artificial intelligence, particularly through **comparing 2024's top machine learning frameworks**. As we delve into the realm of artificial intelligence, **comparing 2024's top machine learning frameworks** becomes essential for Canadian small business owners aiming to leverage technology for growth and efficiency.

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sciencedirect.com article

Comparison of distributed Machine Learning frameworks in a fog environment: Conceptual and Performance analysis - ScienceDirect

https://www.sciencedirect.com/science/article/abs/pii/S2542660525002884

This study conducts a comparative analysis of distributed ML frameworks for neural network training on resource-constrained fog nodes, using Raspberry Pi (RPi) devices. The integration of distributed data analytics within fog computing is essential for obtaining real-time insights from IoT devices [4] as it allows parallel processing of large-scale datasets, which is vital for applications such as autonomous vehicles and industrial automation, where timely and accurate decision making is essential. Developing a generalized distributed framework for training large neural networks and ML models on resource-constrained fog devices by considering various technical parameters, largely remains an understudied problem. In this work, we first focus specifically on actor-model based approaches for distributed neural network training due to their scalability and adaptability in fog computing environments. This work evaluated and compared multiple distributed neural network training frameworks in fog computing environments, focusing on their adaptability to resource-constrained edge devices. ### CANTO: An actor model-based distributed fog framework supporting neural networks training in IoT applications.

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