Ravarani charles (14 Ergebnisse)

Zustand: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Zustand: Very Good. Bridge the gap between modern machine learning and real-world biology with this practical, project-driven guide. Whether your background is in biology, software engineering, or data science, Deep Learning for Biology gives you the tools to develop deep learning models for tackling a wide range of biological problems.Authors Charles Ravarani and Natasha Latysheva guide you through hands-on projects applying deep learning to domains like DNA, proteins, biological networks, medical images, and microscopy. Each chapter is a self-contained mini-project, with step-by-step explanations that teach you how to train and interpret deep learning models using real biological data.Build models for real-world biological problems such as gene regulation, protein function prediction, drug interactions, and cancer detectionApply architectures like convolutional neural networks, transformers, graph neural networks, and autoencodersUse Python and interactive notebooks for hands-on learningBuild problem-solving intuition that generalizes beyond biologyWhether youare exploring new methods, transitioning into computational biology, or looking to make sense of machine learning in your field, this book offers a clear and approachable path forward. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.…

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. In English.

Zustand: New.

Paperback. Zustand: Brand New. 300 pages. 9.19x7.00x9.19 inches. In Stock.

Zustand: New. 2025. paperback. . . . . . Books ship from the US and Ireland.

Zustand: New.

Taschenbuch. Zustand: Neu. Neuware - Bridge the gap between modern machine learning and real-world biology with this practical, project-driven guide. Whether your background is in biology, software engineering, or data science, Deep Learning for Biology gives you the tools to develop deep learning models for tackling a wide range of biological problems. Authors Charles Ravarani and Natasha Latysheva guide you through hands-on projects applying deep learning to domains like DNA, proteins, biological networks, medical images, and microscopy. Each chapter is a self-contained mini-project, with step-by-step explanations that teach you how to train and interpret deep learning models using real biological data. - Build models for real-world biological problems such as gene regulation, protein function prediction, drug interactions, and cancer detection - Apply architectures like convolutional neural networks, transformers, graph neural networks, and autoencoders - Use Python and interactive not Elektronisches Buch for hands-on learning - Build problem-solving intuition that generalizes beyond biology Whether you're exploring new methods, transitioning into computational biology, or looking to make sense of machine learning in your field, this book offers a clear and approachable path forward.…

paperback. Zustand: New.

Taschenbuch. Zustand: Neu. Deep Learning for Biology | Harness AI to Solve Real-World Biology Problems | Charles Ravarani (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2025 | O'Reilly Media | EAN 9781098168032 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.…

Taschenbuch. Zustand: Neu. Neuware -Bridge the gap between modern machine learning and real-world biology with this practical, project-driven guide. Whether your background is in biology, software engineering, or data science, Deep Learning for Biology gives you the tools to develop deep learning models for tackling a wide range of biological problems. Authors Charles Ravarani and Natasha Latysheva guide you through hands-on projects applying deep learning to domains like DNA, proteins, biological networks, medical images, and microscopy. Each chapter is a self-contained mini-project, with step-by-step explanations that teach you how to train and interpret deep learning models using real biological data. - Build models for real-world biological problems such as gene regulation, protein function prediction, drug interactions, and cancer detection - Apply architectures like convolutional neural networks, transformers, graph neural networks, and autoencoders - Use Python and interactive not Elektronisches Buch for hands-on learning - Build problem-solving intuition that generalizes beyond biology Whether you're exploring new methods, transitioning into computational biology, or looking to make sense of machine learning in your field, this book offers a clear and approachable path forward.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 300 pp. Englisch.…

Taschenbuch. Zustand: Neu. Neuware -Bridge the gap between modern machine learning and real-world biology with this practical, project-driven guide. Whether your background is in biology, software engineering, or data science, Deep Learning for Biology gives you the tools to develop deep learning models for tackling a wide range of biological problems. Authors Charles Ravarani and Natasha Latysheva guide you through hands-on projects applying deep learning to domains like DNA, proteins, biological networks, medical images, and microscopy. Each chapter is a self-contained mini-project, with step-by-step explanations that teach you how to train and interpret deep learning models using real biological data. - Build models for real-world biological problems such as gene regulation, protein function prediction, drug interactions, and cancer detection - Apply architectures like convolutional neural networks, transformers, graph neural networks, and autoencoders - Use Python and interactive not Elektronisches Buch for hands-on learning - Build problem-solving intuition that generalizes beyond biology Whether you're exploring new methods, transitioning into computational biology, or looking to make sense of machine learning in your field, this book offers a clear and approachable path forward. 300 pp. Englisch.…