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PS I was using AI before I even went to law school
| Glittery Senate Doctorate | 11/29/25 |
| Glittery Senate Doctorate | 11/29/25 |
| Scarlet exciting bawdyhouse people who are hurt | 11/29/25 |
| Glittery Senate Doctorate | 11/29/25 |
| Glittery Senate Doctorate | 11/29/25 |
| Glittery Senate Doctorate | 11/30/25 |
Poast new message in this thread
Date: November 29th, 2025 2:51 PM Author: Glittery Senate Doctorate This is what we called it back then. "Supervised classification."
https://www.sciencedirect.com/topics/earth-and-planetary-sciences/supervised-classification
Same shit, different name. Still don't think it's a bubble? |
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Date: November 29th, 2025 2:54 PM Author: Glittery Senate Doctorate "Commonly, classification uses a maximum-likelihood classifier to determine the probability that a cell belongs to a class and assigns the cell to the class it is most likely to belong to." (http://www.autoadmit.com/thread.php?thread_id=5804141&forum_id=2Ã#49470555) |
Date: November 29th, 2025 2:55 PM Author: Scarlet exciting bawdyhouse people who are hurt suck my dick niggerfaggot (http://www.autoadmit.com/thread.php?thread_id=5804141&forum_id=2Ã#49470557) |
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Date: November 29th, 2025 2:55 PM Author: Glittery Senate Doctorate A Gentle Introduction to Maximum Likelihood Estimation for Machine Learning By Jason Brownlee on November 5, 2019 in Probability
https://machinelearningmastery.com/what-is-maximum-likelihood-estimation-in-machine-learning/
After reading this post, you will know:
Maximum Likelihood Estimation is a probabilistic framework for solving the problem of density estimation. It involves maximizing a likelihood function in order to find the probability distribution and parameters that best explain the observed data. It provides a framework for predictive modeling in machine learning where finding model parameters can be framed as an optimization problem. |
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Date: November 29th, 2025 2:59 PM Author: Glittery Senate Doctorate MLE is commonly used in logistic regression, Gaussian Mixture Models (GMMs), Hidden Markov Models (HMMs), and Natural Language Processing (NLP). In AI-driven applications, it helps in predictive modeling, speech recognition, and anomaly detection. By finding parameter values that make the observed data most probable, MLE ensures that models generalize well and make reliable predictions.
https://www.appliedaicourse.com/blog/maximum-likelihood-estimation-in-machine-learning/ |
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Date: November 30th, 2025 9:33 PM Author: Glittery Senate Doctorate Unnnnngh
https://x.com/distant_earth83/status/1995033549368373672?t=GESEqbgPvIh_viAj6k4wfw&s=19 |