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Tensorflow- An impressive deep learning library!

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction Tensorflow (hereinafter – TF) is a fairly young framework for deep machine learning, being developed in Google Brain. The post Tensorflow- An impressive deep learning library! appeared first on Analytics Vidhya.

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Getting Started with Docker for Machine Learning

Flipboard

Home Table of Contents Getting Started with Docker for Machine Learning Overview: Why the Need? How Do Containers Differ from Virtual Machines? Finally, we will top it off by installing Docker on our local machine with simple and easy-to-follow steps. What Are Containers?

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TensorFlow vs. PyTorch: What’s Better for a Deep Learning Project?

Towards AI

Photo by Marius Masalar on Unsplash Deep learning. A subset of machine learning utilizing multilayered neural networks, otherwise known as deep neural networks. If you’re getting started with deep learning, you’ll find yourself overwhelmed with the amount of frameworks. Let’s answer that question.

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Tensor Processing Units (TPUs)

Dataconomy

Tensor Processing Units (TPUs) represent a significant leap in hardware specifically designed for machine learning tasks. They are essential for processing large amounts of data efficiently, particularly in deep learning applications.

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Computer Vision and Deep Learning for Education

PyImageSearch

This last blog of the series will cover the benefits, applications, challenges, and tradeoffs of using deep learning in the education sector. To learn about Computer Vision and Deep Learning for Education, just keep reading. As soon as the system adapts to human wants, it automates the learning process accordingly.

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TensorFlow

Dataconomy

TensorFlow has revolutionized the field of machine learning and deep learning since its inception. TensorFlow is an open-source framework designed for machine learning and deep learning applications. Released as open-source in 2015 under the Apache 2.0 in early 2017.

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Backpropagation

Dataconomy

Backpropagation is a cornerstone of machine learning, particularly in the design and training of neural networks. It acts as a learning mechanism, continuously refining model predictions through a process that adjusts weights based on errors. Decision trees: Facilitate intuitive interpretability in machine learning models.