Sebastian Raschka, PhD

Developing good predictive models hinges upon accurate performance evaluation and comparisons.

The cross-entropy loss is our go-to loss for training deep learning-based classifiers.

TorchMetrics is a really nice and convenient library that lets us compute the performance of models in an iterative fashion.

Machine Learning with PyTorch and Scikit-Learn has been a long time in the making, and I am excited to finally get to talk about the release of my new book...

About half a year ago, I organized all my deep learning-related videos in a handy blog post to have everything in one place.

I just sat down this morning and organized all deep learning related videos I recorded in 2021.

With the semester being in full swing, I recently shared this set of dataset repositories with my deep learning class.

After its release in August 2020, Deep Learning with PyTorch has been sitting on my shelf before I finally got a chance to read it during this winter break...

Since I started my undergraduate studies in 2008, I have been obsessed with productivity tips, notetaking solutions, and todo-list management.

Beginner's guide to NumPy and Matplotlib for scientific computing: arrays, indexing, broadcasting, linear algebra, and plotting with Python code examples.

Review of Christoph Molnar's Interpretable Machine Learning, plus practical notes on GAMs, LIME, and SHAP for explaining black-box model predictions.

The first chapter (draft) of the Introduction to Deep Learning book, which is a book based on my lecture notes and slides.

A brief review of Martin Ford's book that features interviews with 23 of the most well-known and brightest minds working on AI.

A brief summary of what's new in the 3rd edition of Python Machine Learning.

Not too long ago, in the Summer of 2018, I was super excited to join the Department of Statistics at the University of Wisconsin-Madison after obtaining my...

Part 4 of the model evaluation series explaining statistical tests, algorithm comparisons, corrected resampled tests, and nested cross-validation.

I thought that it would be nice to have short and concise summaries of recent projects handy, to share them with a more general audience, including...

Part 3 of the model evaluation series covering hyperparameter tuning, model selection, validation sets, k-fold cross-validation, and nested workflows.

Part 2 of the model evaluation series explaining bootstrap methods, holdout validation, resampling variance, uncertainty estimates, and model stability.

Part 1 of a practical model evaluation series covering generalization performance, train-test splits, bias, variance, and supervised learning workflow basics.

It's been about time.

This has really been quite a journey for me lately.

History and fundamentals of single-layer neural networks and gradient descent, with Python implementations of the perceptron and ADALINE for classification.

Step-by-step PCA tutorial that explains standardization, covariance matrices, eigendecomposition, explained variance, and projection with Python code.

Here, I want to present a simple and conservative approach of implementing a weighted majority rule ensemble classifier in scikit-learn that yielded...

Music mood classification project: predicting a song's emotional valence from lyrics with NLP, feature engineering, and scikit-learn in Python.

Last week, I posted some visualizations in context of Happy Rock Song data mining project, and some people were curious about how I created the word clouds...

Naive Bayes classifiers, a family of classifiers that are based on the popular Bayes’ probability theorem, are known for creating simple yet well performing...

Tutorial on kernel methods and nonlinear dimensionality reduction with RBF kernel PCA, including the kernel trick and a Python implementation.

When I was working on my next pattern classification application, I realized that it might be worthwhile to take a step back and look at the big picture of...

Step-by-step Linear Discriminant Analysis tutorial covering scatter matrices, eigenvectors, class separation, dimensionality reduction, and Python code.

I recently faced the impossible task to identify outliers in a dataset with very, very small sample sizes and Dixon's Q test caught my attention.

I received a couple of questions in response to my previous article (Entry point: Data) where people asked me why I used Z-score standardization as feature...

Overview of Python tools for data entry and pattern recognition workflows — file I/O, CSV handling, argparse, and command-line scripting best practices.

Discussions and questions about methods, approaches, and tools for estimating (relative) binding free energies of protein-ligand complexes are quite...

Parallel programming in Python with multiprocessing: bypassing the GIL, spawning processes, using Pool.map, and speeding up CPU-bound workloads.

At its core, this article is about a simple cheat sheet for basic operations on numeric matrices, which can be very useful if you working and experimenting...

The Parzen-window method (also known as Parzen-Rosenblatt window method) is a widely used non-parametric approach to estimate a probability density function...

Many beginning Python users are wondering with which version of Python they should start.

5-step tutorial to add code syntax highlighting to Markdown blog posts using Python-Markdown, Pygments, and custom CSS stylesheets.

Many people have asked me how I create the table of contents with internal links for my IPython Notebooks and Markdown documents on GitHub.

A short tutorial about Python's namespaces and the scope resolution for variable names using the LEGB-rule with little quiz-like exercises.

Some while ago, I started to collect some of the not-so-obvious things I encountered when I was coding in Python.

Implementing Principal Component Analysis from scratch in Python: scatter matrices, eigenvectors, variance explained, and comparison with scikit-learn PCA.

I just went through some pain (again) when I wanted to install some of Python's scientific libraries on my second Mac.

Complete SQLite guide for Python: creating databases, running queries, bulk inserts, pandas integration, and tips for working with large datasets.

This is a quickguide showing how to use OpenEye software command line tools to align target molecules to a query based on substructure matches and how to...

Let’s be honest, code testing is everything but a joyful task.

I received many questions from people who want to quickly visualize their data via heat maps - ideally as quickly as possible.

My new project confronted me with the task of screening a massive set of large data files in text format with billions of entries each.