A data scientist's journey from dogmatic Bayesianism to a pragmatic, 'secular' use of Bayesian tools without requiring belief in the model's literal existence.
A speaker's review of PyGotham 2019, covering talk quality, event organization, and highlights like a talk on web archiving tools.
A tutorial on implementing image classification in a React Native app using TensorFlow.js and the MobileNet pre-trained model.
A case study on building and deploying a machine learning system for hospital bill estimation, reducing prediction errors by over 50%.
Explores AI-driven content curation through Archillect and algorithmic generative art via Inconvergent, highlighting automated creativity.
A keynote presentation on scaling tech platforms and the SuperApp strategy, using case studies from Alibaba, Grab, and WeChat.
An exploration of predictive analytics, its historical roots in human nature, and its modern implementation through data science and AI technologies.
A curated list of free resources for data mining, analysis, and visualization, including books, tools, libraries, and tutorials.
Explains the theory behind linear regression models, a fundamental machine learning algorithm for predicting continuous numerical values.
A tutorial on building a React Native app that uses Google's Vision API to classify images as 'hotdog' or 'not hotdog', inspired by HBO's Silicon Valley.
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.
