Practical Deep Learning, 2nd Edition

Ronald T. Kneusel

ISBN 10: 1718504209 / ISBN 13: 9781718504202

Editore: No Starch Press,US, US, 2025

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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, Practical Deep Learning, 2nd Edition teaches you the why of deep learning and will inspire you to explore further. 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: How to use classic machine learning models like k-Nearest Neighbours, Random Forests, and Support Vector Machines, How neural networks work and how they're trained, How to use convolutional neural networks, How to develop a successful deep learning model from scratch. 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, Practical Deep Learning, 2nd Edition will give you the skills and confidence to dive into your own machine learning projects.

Codice articolo LU-9781718504202

Dati bibliografici

• Titolo: Practical Deep Learning, 2nd Edition
• Editore: No Starch Press,US, US
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 1718504209
• ISBN 13: 9781718504202
• Rilegatura: Paperback
• Edizione: seconda edizione
• Condizione: New

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Riassunto

Practical Deep Learning, 2nd Edition is your gateway into AI, equipping you with the knowledge and confidence to build powerful AI models using the latest architectures and techniques.

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, Practical Deep Learning, 2nd Edition teaches you the why of deep learning and will inspire you to explore further.

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: 

• How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines
• How neural networks work and how they’re trained
• How to use convolutional neural networks
• How to develop a successful deep learning model from scratch

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, Practical Deep Learning, 2nd Edition will give you the skills and confidence to dive into your own machine learning projects.

Informazioni sull?autore

Ronald T. Kneusel earned a PhD in machine learning from the University of Colorado, Boulder, and has over 20 years of machine learning experience in industry. Kneusel is also the author of numerous books, including Math for Programming (2025), The Art of Randomness (2024), How AI Works (2023), Strange Code (2022), and Math for Deep Learning (2021), all from No Starch Press.