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The Rise of AI-Powered Text Messaging in Business

Analytics Vidhya

Introduction In recent years, the integration of Artificial Intelligence (AI), specifically Natural Language Processing (NLP) and Machine Learning (ML), has fundamentally transformed the landscape of text-based communication in businesses.

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Supercharge your skill set with 9 free machine learning courses

Data Science Dojo

Machine learning courses are not just a buzzword anymore; they are reshaping the careers of many people who want their breakthrough in tech. From revolutionizing healthcare and finance to propelling us towards autonomous systems and intelligent robots, the transformative impact of machine learning knows no bounds.

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Counting shots, making strides: Zero, one and few-shot learning unleashed 

Data Science Dojo

In the dynamic field of artificial intelligence, traditional machine learning, reliant on extensive labeled datasets, has given way to transformative learning paradigms. Source: Photo by Hal Gatewood on Unsplash In this exploration, we navigate from the basics of supervised learning to the forefront of adaptive models.

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Knowledge Distillation: Making AI Models Smaller, Faster & Smarter

Data Science Dojo

Knowledge Distillation is a machine learning technique where a teacher model (a large, complex model) transfers its knowledge to a student model (a smaller, efficient model). Now, it is time to train the teacher model on the dataset using standard supervised learning. What Is Knowledge Distillation?

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Generative vs Discriminative AI: Understanding the 5 Key Differences

Data Science Dojo

A visual representation of generative AI – Source: Analytics Vidhya Generative AI is a growing area in machine learning, involving algorithms that create new content on their own. This approach involves techniques where the machine learns from massive amounts of data.

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Delineating the effective use of self-supervised learning in single-cell genomics

Flipboard

Self-supervised learning (SSL) has emerged as a powerful method for extracting meaningful representations from vast, unlabelled datasets, transforming computer vision and natural language processing. Richter et al.

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How Faulty Data Breaks Your Machine Learning Process

Dataconomy

To learn more about this topic, please consider attending our fourth annual PyData Berlin conference on June 30-July 2, 2017. The post How Faulty Data Breaks Your Machine Learning Process appeared first on Dataconomy. Miroslav Batchkarov and other experts will be giving talks.