Machine Learning – From Foundations to Advanced Architectures The field of Machine Learning can be overwhelming, and you might feel like you're losing control. Instead of giving up, let me share a secret with you: it's easier than you think. All you need is a kind of “Rosetta Stone” to fully accelerate your work with machine learning. This book offers such a guide. It takes you on an alternative route, starting with fundamental concepts from calculus, linear algebra, numerical methods, and optimization, and leading up to the state-of-the-art algorithms that have emerged over the last couple of decades. My background in industry, research, and teaching has given me deep insights into the common challenges of developing efficient algorithms for prediction. That experience is distilled here into a coding-oriented, “from-scratch” approach, where we focus on setting up the environment properly, defining datasets, configuring training and validation, and avoiding version-related pitfalls that often cause problems in real-world projects. Balancing conceptual clarity with practical implementation, the book combines detailed derivations, clear illustrations, and runnable Python code. It is designed for students, engineers, and researchers who want to not only understand the principles of machine learning but also build systems that work. Contents (main chapters): • Nomenclature • Foreword • Introduction • Numerical Methods – The Basis for ML • Neural Networks (ANN) • ResNet • Autograd • Convolutional Neural Networks (CNN) • Activation Functions • System Analysis – LeNet-5 Case Study • Transformers • Discrete Wavelet Transform • Support Vector Machines (SVM) • Principal Component Analysis (PCA) • Generative Adversarial Networks (GAN) • Spiking Neural Networks (SNN) • Equivariant CNNs • Data Structures for Object Detection • Glossary of AI and Machine Learning Terms • Bibliography
