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Book Review: Math for Deep Learning

insideBIGDATA

One of my favorite learning resources for gaining an understanding for the mathematics behind deep learning is "Math for Deep Learning" by Ronald T. If you're interested in getting quickly up to speed with how deep learning algorithms work at a basic level, then this is the book for you.

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Deep Learning Approaches in Medical Image Segmentation

KDnuggets

Medical imaging has been revolutionized by the adoption of deep learning techniques. The use of this branch of machine learning has ushered in a new era of precision and efficiency in medical image segmentation, a central analytical process in modern healthcare diagnostics and treatment planning.

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PyTorch vs TensorFlow: Which is Better for Deep Learning?

Analytics Vidhya

Introduction Efficient ML models and frameworks for building or even deploying are the need of the hour after the advent of Machine Learning (ML) and Artificial Intelligence (AI) in various sectors. PyTorch and Tensorflow have similar features, integrations, […] The post PyTorch vs TensorFlow: Which is Better for Deep Learning?

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7 Libraries for Machine Learning

Analytics Vidhya

Introduction Machine learning has revolutionized the field of data analysis and predictive modelling. With the help of machine learning libraries, developers and data scientists can easily implement complex algorithms and models without writing extensive code from scratch.

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Rust Burn Library for Deep Learning

KDnuggets

A new deep learning framework built entirely in Rust that aims to balance flexibility, performance, and ease of use for researchers, ML engineers, and developers.

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The Only Interview Prep Course You Need for Deep Learning

KDnuggets

Dive into the 50 most popular deep-learning questions to get you ready for your interview.

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Deploying Machine Learning Models at Scale: Strategies for Efficient Production

insideBIGDATA

In this contributed article, freelance writer Ainsley Lawrence briefly explores deploying machine learning models, showing you how to manage multiple models, establish robust monitoring protocols, and efficiently prepare to scale.