A technical guide on computing distance matrices using NumPy, focusing on Euclidean distance and its application in machine learning algorithms like k-Nearest Neighbors.
Explains the theory behind Linear Regression, a fundamental machine learning model for predicting continuous numerical values.
A data science leader shares insights from a fireside chat on building and running data teams, focusing on their role as profit centers and collaboration strategies.
A podcast episode exploring life lessons derived from machine learning concepts like data cleaning, explore-exploit, and overfitting.
A guide on writing effective design documents for machine learning systems, covering structure, purpose, and a two-stage review process.
A technical guide on building an indoor location prediction system using WiFi signal data and a Random Forest classifier in JavaScript.
Explores the concept of feature stores in machine learning, presenting a hierarchy of needs from basic access to full automation.
A curated list of public dataset repositories for machine learning and deep learning projects, including sources for computer vision, NLP, and more.
A curated list of public dataset repositories for machine learning and deep learning projects, including computer vision and NLP datasets.
A behind-the-scenes look at designing and implementing a production machine learning system for a major hospital group, covering architecture and validation.
Announcing the 2021 Ihaka Lectures featuring local experts on distributed computing, machine learning for child welfare, and applied math for COVID-19 response.
A data science leader shares insights on hiring, training, and managing effective data science teams, based on experience at Lazada and uCare.
Explains the theory behind linear regression models, a fundamental machine learning algorithm for predicting continuous numerical values.
Argues that taking more MOOCs has diminishing returns for tech professionals and advocates for hands-on, project-based learning instead.
A detailed review of the book 'Deep Learning with PyTorch,' covering its structure, content, and suitability for students and practitioners.
A review of the book 'Deep Learning with PyTorch', covering its structure, content, and suitability for students and beginners in deep learning.
A podcast transcript discussing the importance of writing for career growth in tech, covering motivation, process, and Amazon's writing culture.
A technical deep dive into real-time machine learning for recommendation systems, comparing approaches in China and the US and discussing implementation.
A data scientist's 2020 review, focusing on machine learning projects for healthcare, including mining COVID-19 EHR data and brain signal analysis.
A data scientist's 2020 reflection on moving to Amazon, building ML systems, and establishing a weekly writing habit for learning and sharing knowledge.
