Sebastian Raschka, PhD, is an LLM Research Engineer and AI expert bridging academia and industry, specializing in large language models, high-performance AI systems, and practical, code-driven machine learning.
103 articles from this blog
A guide to evaluating machine learning models, selecting the best models, and choosing appropriate algorithms to ensure good generalization performance.
Author shares the journey and process of writing a book on Python Machine Learning, including productivity tips and the book's focus.
A scientist explains why Python is their preferred tool for machine learning and data analysis, emphasizing productivity over language wars.
An introduction to single-layer neural networks, covering the history, perceptrons, adaptive linear neurons, and the gradient descent algorithm with Python implementations.
A tutorial explaining the internals of Principal Component Analysis (PCA) for dimensionality reduction in machine learning and data analysis.
A guide to building a weighted majority rule ensemble classifier in scikit-learn, demonstrated using the Iris dataset.
A developer shares a data mining project that builds a machine learning model to classify songs as happy or sad based on their lyrics.
A Python tutorial showing how to download your Twitter timeline and visualize it as a word cloud using data science libraries.
Explores Naive Bayes classifiers for text classification, covering theory and applications like spam filtering and song lyric analysis.
Explains how to use the RBF kernel trick to perform nonlinear dimensionality reduction via Kernel PCA for complex datasets.
An overview of predictive modeling, supervised machine learning, and the core workflow for pattern classification tasks.
A technical guide to Linear Discriminant Analysis (LDA) for dimensionality reduction and classification in machine learning, with comparisons to PCA.
A technical guide to Dixon's Q test for identifying outliers in small datasets, including its method, application, and criticisms.
Explains feature scaling and normalization in machine learning, comparing standardization and Min-Max scaling, with examples using scikit-learn.
A tutorial on using Python tools for machine learning, covering data loading, visualization, preprocessing, and classification with scikit-learn.
A technical overview of molecular docking, focusing on using AutoDock 4.2 to estimate protein-ligand binding free energies and comparing scoring functions.
A guide to using Python's multiprocessing module for parallel programming to overcome the GIL and utilize multi-core CPUs.
A 5-step tutorial on converting Markdown to HTML with Python, adding syntax highlighting for code blocks using Python-Markdown and Pygments.
A tutorial on creating internal links and a table of contents in IPython Notebooks and Markdown documents using HTML anchors.
A technical guide on using OpenEye's command-line tools for molecular substructure alignment and low-energy conformer overlay workflows.
