A guide to implementing vectorized gradient descent in JavaScript for machine learning, improving efficiency over unvectorized approaches.
Explores methods to optimize the gradient descent algorithm in JavaScript, focusing on selecting the right learning rate for convergence.
A recap of PyData Warsaw 2017, covering key talks, new package announcements, and analytics on the conference's international attendees.
An overview of Machine Learning applications in Remote Sensing, covering key algorithms and the typical workflow for data analysis.
Explains polynomial regression as a solution to under-fitting in machine learning when data has a nonlinear correlation.
A guide to implementing linear algebra concepts and matrix operations in JavaScript, using the math.js library for machine learning.
A guide to implementing linear regression with gradient descent in JavaScript, using a housing price prediction example.
A guide for beginners on how to start learning deep learning using the Keras library, including recommended resources and prerequisites.
A researcher's 2016 highlights: AI mapping to human vision, brain-based autism prediction, and fast matrix factorization algorithms for neuroimaging.
A guide to model evaluation, selection, and algorithm comparison in machine learning to ensure models generalize well to new data.
A guide for academics with math/physics backgrounds transitioning into data science, covering skills, learning paths, and practical advice.
Explores the importance of reproducible science in computer science, focusing on reproducibility, replicability, and reusability of software and data.
A former PhD scientist shares his positive transition to data science freelancing, detailing the freedom and variety of his new career.
Nilearn 0.2 release enhances machine learning for neuroimaging with new spatial regularizations, dictionary learning, and improved visualization tools.
A post-doc position in computational neuroscience using Python and machine learning to find biomarkers from fMRI brain connectivity data.
Summary of the 2015 MLOSS workshop on open-source machine learning software, covering key talks and the maturing community.
Author shares the journey and process of writing 'Python Machine Learning,' a technical book for aspiring machine learning practitioners.
A scientist explains why Python is their preferred language for machine learning and data analysis, arguing for productivity over language wars.
A summary of the second Nilearn sprint, highlighting new features and improvements for this neuroimaging machine learning library.
Announcing the MLOSS workshop at ICML 2015, focusing on open-source software and ecosystems for machine learning.
