Practical Deep Learning, 2nd Edition A Python-Based Introduction

If you’ve been curious about artificial intelligence and machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, All you need is basic familiarity with computer programming and high school math--the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance. You’ll also learn:; You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. This second edition is thoroughly revised and updated, and adds six new chapters to further your exploration of deep learning from basic CNNs to more advanced models. New chapters cover fine tuning, transfer learning, object detection, semantic segmentation, multilabel classification, self-supervised learning, generative adversarial networks, and large language models. The perfect introduction to this dynamic, ever-expanding field,

Info autore

Ronald T. Kneusel

Dettagli sul prodotto

• Autori: Kneusel Ronald T., Ronald T. Kneusel, Ronald T Kneusel
• Editore: No Starch Press
• Contenuto: Libro
• Forma del prodotto: Tascabile
• Data pubblicazione: 08.07.2025
• Categoria: Scienze naturali, medicina, informatica, tecnica > Informatica, EDP > Informatica
• EAN: 9781718504202
• ISBN: 978-1-71850-420-2
• Numero di pagine: 584
• Dimensioni (della confezione): 17.9 x 24.8 x 2.7 cm
• Categorie: machine learning, Programming & scripting languages: general, COMPUTERS / Data Science / Machine Learning, Programming and scripting languages: general, COMPUTERS / Data Science / Neural Networks, COMPUTERS / Languages / Python, Neural networks and fuzzy systems, Neural networks & fuzzy systems