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Generative vs Discriminative AI: Understanding the 5 Key Differences

Data Science Dojo

Released in 2018, Duplex garnered attention for its ability to handle real-world scenarios, such as making restaurant reservations, with remarkable accuracy and naturalness.

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Are AI technologies ready for the real world?

Dataconomy

Common choices include neural networks (used in deep learning), decision trees, support vector machines, and more. AI practitioners choose an appropriate machine learning model or algorithm that aligns with the problem at hand. With the model selected, the initialization of parameters takes place.

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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.

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Top 5 Generative AI Integration Companies to drive Customer Support in 2023

Chatbots Life

Simple chatbots without generative AI integration rely on pre-programmed responses and rule-based decision trees to guide their interactions with users. By integrating generative AI, chatbots can generate more natural and human-like responses, allowing for a more engaging and satisfying user experience.

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Explainability in AI and Machine Learning Systems: An Overview

Heartbeat

The " Decision Tree " is a popular example of the rule-based model that offers interpretable insights into how the model arrives at its decisions. Decision trees can be trained and visualized in rule-based explanations to reveal the underlying decision logic. Russell, C. & & Watcher, S.

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What a data scientist should know about machine learning kernels?

Mlearning.ai

Gaussian kernels are commonly used for classification problems that involve non-linear boundaries, such as decision trees or neural networks. Laplacian Kernels Laplacian kernels, also known as Laplacian of Gaussian (LoG) kernels, are used in decision trees or neural networks like image processing for edge detection.