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.
97 articles from this blog
Explains the difference between .update() and .forward() methods in the TorchMetrics library for evaluating PyTorch models.
Announcing a new book on machine learning, covering fundamentals with scikit-learn and deep learning with PyTorch, including neural networks from scratch.
A comprehensive list of 90 machine learning lecture videos covering topics from Python basics to advanced ML concepts like decision trees and Bayesian methods.
A comprehensive deep learning course overview with PyTorch tutorials, covering fundamentals, neural networks, and advanced topics like CNNs and GANs.
A curated list of public dataset repositories for machine learning and deep learning projects, including sources for computer vision, NLP, and more.
A detailed review of the book 'Deep Learning with PyTorch,' covering its structure, content, and suitability for students and practitioners.
A software developer shares his personal digital workflow for organizing projects, notes, and tasks using a simple folder system and syncing tools.
A tutorial on using NumPy for numerical arrays and Matplotlib for data visualization in Python, aimed at scientific computing and machine learning.
A review and tutorial covering Christoph Molnar's book on Interpretable Machine Learning, with Python code examples for linear and logistic regression.
An introductory chapter on machine learning and deep learning, covering core concepts, categories, and terminology from a university course.
A review of 'Architects of Intelligence,' a book featuring interviews with 23 leading AI researchers and industry experts.
Announcing the 3rd edition of Python Machine Learning, updated for TensorFlow 2.0 and featuring a new chapter on Generative Adversarial Networks (GANs).
A professor reflects on teaching new Machine Learning and Deep Learning courses at UW-Madison and showcases student projects from those classes.
Explores using semi-adversarial neural networks to generate gender-neutral face images, enhancing privacy by hiding gender while preserving biometric utility.
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.
