Explains how prior probabilities are learned and updated in logistic regression models, using a coffee brewing example to illustrate class imbalance.
Announcement for SQLSaturday Lisbon 2019 and Porto 2019, including dates, call for speakers, and workshop topics.
Explains the theory behind linear regression models, a fundamental machine learning algorithm for predicting continuous numerical values.
A crash course on the theory behind linear regression models, a fundamental machine learning algorithm for predicting numerical values.
Explores the visual similarities between images generated by neural networks and human experiences in dreams or under psychedelics.
A blog post exploring the parallels and differences between human cognition and machine learning, including biases and inspirations.
Explores meta reinforcement learning, where agents learn to adapt quickly to new, unseen RL tasks, aiming for general-purpose problem-solving algorithms.
Explains the mathematical derivation of logistic regression from Bayes' theorem, connecting fundamental statistics to machine learning.
A professor reflects on teaching new Machine Learning and Deep Learning courses at UW-Madison and showcases impressive student projects.
A professor reflects on teaching new Machine Learning and Deep Learning courses at UW-Madison and showcases student projects from those classes.
A review and tips for the OMSCS CS7646 Machine Learning for Trading course, covering the author's experience and key takeaways.
A data scientist clarifies common misconceptions about the field, explaining that machine learning is only a small part of the job and advanced degrees aren't always required.
A data scientist discusses the importance of a business mindset, prioritization, and effective communication for creating real-world value in data science projects.
A deep dive into designing and implementing a Multilayer Perceptron from scratch, exploring the core concepts of neural network architecture and training.
Analysis of PHP's limitations for machine learning, focusing on visualization, Jupyter support, and GPU capabilities compared to Python.
A tutorial on text data classification using the BBC news dataset and PHP-ML for machine learning, covering data loading and preprocessing.
A team shares lessons from a large ML project on organizing code, data, and collaboration using R packages and multi-user server setups.
Explores the paradox of why deep neural networks generalize well despite having many parameters, discussing theories like Occam's Razor and the Lottery Ticket Hypothesis.
Learn to implement the k-Nearest Neighbors algorithm in PHP to predict air quality using public data and the php-ml library.
A case study on building a production ML system to predict patient hospitalization costs for Southeast Asia's largest healthcare group.
