Master Machine Learning : Master Scikit-learn algorithms and PyTorch deep learning architectures
Description: Machine learning is transforming industries from healthcare to finance, and Python has become the lingua franca for building intelligent systems. PyTorch and Scikit-learn are two of the most powerful frameworks driving today's AI revolution, enabling developers to build everything...
Wedi'i Gadw mewn:
| Prif Awdur: | |
|---|---|
| Fformat: | Livre numérique |
| Iaith: | Anglais |
| Cyhoeddwyd: |
New Delhi :
BPB Publications
2026.
Paris : Cyberlibris |
| Mynediad Ar-lein: | Accès Université d'Orléans et IFPM |
| Nodyn: |
Couverture. https://static2.cyberlibris.com/books_upload/300pix/9789378544101.jpg Cyberlibris (ScholarVox) corpus Informatique |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Master Machine Learning, Master Scikit-learn algorithms and PyTorch deep learning architectures, Valencia Munoz Luis, New Delhi, BPB Publications, 2026, 1 vol. (665 p.), 978-93-7854-410-1 |
| LEADER | 04113nam a22002177a 4500 | ||
|---|---|---|---|
| 001 | 1478027 | ||
| 008 | 260708s2026 xxg ||| |||| 00| 0 eng d | ||
| 009 | PPN29802361X | ||
| 041 | 0 | |a eng | |
| 100 | 1 | |a Munoz Luis, Valencia. | |
| 245 | 1 | 0 | |a Master Machine Learning : |b Master Scikit-learn algorithms and PyTorch deep learning architectures |c Valencia Munoz Luis. |
| 260 | |a New Delhi : |b BPB Publications. | ||
| 260 | |a Paris : |b Cyberlibris, |c 2026. | ||
| 500 | |a Couverture. https://static2.cyberlibris.com/books_upload/300pix/9789378544101.jpg | ||
| 500 | |a Cyberlibris (ScholarVox) corpus Informatique | ||
| 505 | 0 | |a 1. Introduction to the Machine Learning World -- 2. Setting up Your Machine Learning Environment -- 3. Python Fundamentals for Machine Learning -- 4. Essential Machine Learning Libraries in Python -- 5. Introduction to Machine Learning with Scikit-learn -- 6. Machine Learning with Scikit-learn Advanced Topics -- 7. Introduction to Deep Learning -- 8. Introduction to PyTorch -- 9. Building Blocks of Neural Networks in PyTorch -- 10. Training Neural Networks with PyTorch -- 11. Convolutional Neural Networks with PyTorch -- 12. Recurrent Neural Networks with PyTorch -- 13. Generative Adversarial Networks with PyTorch -- 14. Reinforcement Learning with PyTorch -- 15. Advanced Deep Learning Topics -- 16. Building a Recommendation System -- 17. Natural Language Processing with PyTorch -- 18. Computer Vision with PyTorch -- 19. Time Series Analysis with PyTorch -- 20. Deploying Machine Learning Models | |
| 506 | |a L'accès en ligne est réservé aux établissements ou bibliothèques ayant souscrit l'abonnement. Cyberlibris | ||
| 520 | |a Description: Machine learning is transforming industries from healthcare to finance, and Python has become the lingua franca for building intelligent systems. PyTorch and Scikit-learn are two of the most powerful frameworks driving today's AI revolution, enabling developers to build everything from simple predictive models to sophisticated deep learning architectures. This book takes you on a comprehensive journey from Python fundamentals through advanced deep learning. You will master essential libraries like NumPy, Pandas, and Matplotlib, and build classical ML models with Scikit-learn before exploring neural networks with PyTorch. Through 20 hands-on chapters, you will explore CNNs, RNNs, GANs, reinforcement learning, transformers, recommendation systems, NLP, time series analysis, and finally deploy models to Azure ML as production-ready APIs. By the end of this book, you will have the hands-on expertise to build, train, and deploy advanced AI systems. Whether you are starting your ML journey or deepening your skills, you will gain the confidence to tackle real-world challenges and contribute meaningfully to the field of artificial intelligence. What you will learn: Set up professional ML environments locally and in the cloud; Build and evaluate ML models using Scikit-learn algorithms; Design neural networks from scratch using the PyTorch framework; Implement CNNs, RNNs, GANs, and reinforcement learning systems; Apply NLP and computer vision techniques to real-world problems; Build recommendation systems and time series forecasting models; Deploy trained models to Azure ML as production REST APIs. Who this book is for: This book is for Python developers, data scientists, and engineers aiming to master AI. Beginners and professionals should possess basic Python knowledge before exploring Scikit-learn and PyTorch to build, optimize, and deploy production-ready machine learning models across diverse industrial applications | ||
| 776 | 0 | |t Master Machine Learning |o Master Scikit-learn algorithms and PyTorch deep learning architectures |f Valencia Munoz Luis |c New Delhi |n BPB Publications |d 2026 |p 1 vol. (665 p.) |z 978-93-7854-410-1 | |
| 856 | 4 | |5 452349901:894031163 |u https://ezproxy.univ-orleans.fr/login?url=https://univ.scholarvox.com/book/88980630 |z Accès Université d'Orléans et IFPM | |
| 997 | |0 1478027 |1 Livre numérique |a Ressource numérique |b INSA |b ENSA |c 0/Bibliothèque numérique/ |c 1/Bibliothèque numérique/ScholarVox (ebooks)/ | ||