SebastianRaschka.com is the personal blog of Sebastian Raschka, PhD, an LLM research engineer whose work bridges academia and industry in AI and machine learning. On his blog and notes section he publishes deep, well-documented articles on topics such as LLMs (large language models), reasoning models, machine learning in Python, neural networks, data science workflows, and deep learning architecture. Recent posts explore advanced themes like “reasoning LLMs”, comparisons of modern open-weight transformer architectures, and guides for building, training, or analyzing neural networks and model internals.
98 articles from this blog
An overview of predictive modeling, supervised machine learning, and pattern classification concepts, workflows, and applications.
A technical guide to Linear Discriminant Analysis (LDA) for dimensionality reduction and classification in machine learning, including a Python implementation.
A guide to feature scaling and normalization in machine learning, covering standardization, Min-Max scaling, and their implementation in scikit-learn.
A Python tutorial covering essential tools and techniques for machine learning, including data visualization, PCA, LDA, and classification.
Learn how to use Python's multiprocessing module for parallel programming to overcome the GIL and utilize multiple CPU cores effectively.
A cheat sheet comparing matrix operations across MATLAB/Octave, Python (NumPy), R, and Julia for scientific computing and data analysis.
A technical guide to the Parzen-Rosenblatt window method for non-parametric kernel density estimation, including implementation and applications.
A guide explaining the key differences between Python 2.7.x and Python 3.x, covering syntax changes, features, and common pitfalls.
A 5-step tutorial on converting Markdown to HTML with Python syntax highlighting using Python-Markdown and Pygments.
A tutorial on creating tables of contents with internal links in IPython Notebooks and Markdown documents using HTML anchors.
A tutorial explaining Python namespaces, scope resolution, and the LEGB rule for variable lookup with practical examples.
An in-depth exploration of Python's advanced features, quirks, and common pitfalls for experienced developers.
A technical guide to implementing Principal Component Analysis (PCA) for dimensionality reduction, comparing it with MDA and providing code examples.
A guide to installing Python scientific libraries (NumPy, SciPy, matplotlib) on macOS 10.9, covering both Anaconda/Miniconda and manual pip installation methods.
A comprehensive guide to performing SQLite database operations in Python using the sqlite3 module, from setup to queries.
A guide to unit testing in Python, covering its benefits, components, and a practical walkthrough using the py.test framework.
A tutorial on creating customizable heat maps in R using the gplots and RColorBrewer packages for data visualization.
A guide to using SQLite and Python's sqlite3 module to efficiently manage and query large datasets from text files.
