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.
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.
A technical exploration of Mean Squared Error, breaking it down into bias and variance to understand model performance and irreducible uncertainty.
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.
