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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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How To Improve Machine Learning Model Accuracy

DagsHub

In 2018, there were extensive news reports that an Uber self-driving car made an accident with a pedestrian in Tempe, Arizona. The pedestrian died, and investigators found that there was an issue with the machine learning (ML) model in the car, so it failed to identify the pedestrian beforehand. These are: 1.

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Explainability in AI and Machine Learning Systems: An Overview

Heartbeat

This guide will buttress explainability in machine learning and AI systems. The explainability concept involves providing insights into the decisions and predictions made by artificial intelligence (AI) systems and machine learning models. What is Explainability?

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What a data scientist should know about machine learning kernels?

Mlearning.ai

Photo by Robo Wunderkind on Unsplash In general , a data scientist should have a basic understanding of the following concepts related to kernels in machine learning: 1. Machine learning algorithms rely on mathematical functions called “kernels” to make predictions based on input data. What are kernels? Linear Kernels 2.

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Are AI technologies ready for the real world?

Dataconomy

AI has made significant contributions to various aspects of our lives in the last five years ( Image credit ) How do AI technologies learn from the data we provide? AI technologies learn from the data we provide through a structured process known as training. Another form of machine learning algorithm is known as unsupervised learning.

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Transformer Models: The future of Natural Language Processing

Data Science Dojo

2018: Transformer models achieve state-of-the-art results on a wide range of NLP tasks, including machine translation, text summarization, and question answering. Interpretability: Transformer models are not as interpretable as other machine learning models, such as decision trees and logistic regression.

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Transformer Models: The future of Natural Language Processing

Data Science Dojo

2018: Transformer models achieve state-of-the-art results on a wide range of NLP tasks, including machine translation, text summarization, and question answering. Interpretability: Transformer models are not as interpretable as other machine learning models, such as decision trees and logistic regression.