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ML and AI Model Explainability and Interpretability

Analytics Vidhya

Through tools like LIME and SHAP, we demonstrate how to gain insights […] The post ML and AI Model Explainability and Interpretability appeared first on Analytics Vidhya.

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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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Introduction to Apple’s Core ML 3 – Build Deep Learning Models for the iPhone (with code)

Analytics Vidhya

Overview Apple’s Core ML 3 is a perfect segway for developers and programmers to get into the AI ecosystem You can build machine learning. The post Introduction to Apple’s Core ML 3 – Build Deep Learning Models for the iPhone (with code) appeared first on Analytics Vidhya.

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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. Although there are several frameworks, PyTorch and TensorFlow emerge as the most famous and commonly used ones.

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Artificial Intelligence Vs Machine Learning Vs Deep Learning: What exactly is the difference ?

Analytics Vidhya

Artificial Intelligence, Machine Learning and, Deep Learning are the buzzwords of. The post Artificial Intelligence Vs Machine Learning Vs Deep Learning: What exactly is the difference ? ArticleVideo Book This article was published as a part of the Data Science Blogathon.

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Hydra Configs for Deep Learning Experiments

KDnuggets

This brief guide illustrates how to use the Hydra library for ML experiments, especially in the case of deep learning-related tasks, and why you need this tool to make your workflow easier.

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On-Device Deep Learning: PyTorch Mobile and TensorFlow Lite

KDnuggets

PyTorch and TensorFlow are the two leading AI/ML Frameworks. In this article, we take a look at their on-device counterparts PyTorch Mobile and TensorFlow Lite and examine them more deeply from the perspective of someone who wishes to develop and deploy models for use on mobile platforms.