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.
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.
Tutorial on building a React Native app that uses Google Cloud Vision API for image recognition, including Firebase setup.
A guide to using Python decorators for automatic TensorFlow named scopes, improving code organization and TensorBoard visualization.
Explores the challenge of machine learning models recognizing 'unknown' inputs, using mushroom classification as an example.
Explores handling Out-of-Vocabulary (OOV) values in machine learning, using deep learning for dynamic data in recommender systems as an example.
Explains how Graph Neural Networks and node2vec use graph structure and random walks to generate embeddings for machine learning tasks.
A summary of a panel discussion on various data roles (data scientist, ML engineer, etc.), including key skills and career insights.
Announcement for a lecture series on machine learning, covering topics like Weka, deep learning, algorithmic fairness, and sparse supervised learning.
A researcher's 2018 highlights: using machine learning for cognitive brain mapping, analyzing non-curated data, and contributing to scikit-learn development.
A guide on building a personal brand as a data scientist, covering path selection, blogging, and sharing knowledge within the community.
A review and tips for the challenging OMSCS CS6601 Artificial Intelligence course, covering its content, workload, and personal experience.
A data scientist details how a flawed train-test split method introduced bias when adding image thumbnails to a content recommendation model.
An introduction to meta-learning, a machine learning approach where models learn to adapt quickly to new tasks with minimal data, like 'learning to learn'.
Overview of new features, changes, and fixes in PHP-ML 0.7.0, a machine learning library for PHP developers.
An introduction to flow-based deep generative models, explaining how they explicitly learn data distributions using normalizing flows, compared to GANs and VAEs.
