Buch, Englisch, 336 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 705 g
Updates and Future Prospects
Buch, Englisch, 336 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 705 g
ISBN: 978-981-1688-80-5
Verlag: Springer Nature Singapore
It is of relevance to post-graduate students and researchers interested in exploring the interdisciplinary areas of use of machine learning and deep learning in life sciences.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
- Naturwissenschaften Biowissenschaften Biowissenschaften Systembiologie
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
- Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe
Weitere Infos & Material
Chapter 1. A Brief Overview Of Applications Of Machine Learning In Life Sciences.- Chapter 2. Introduction To Artificial Intelligence (Ai) Methods In Biology.- Chapter 3. Machine Learning Methods.- Chapter 4. Introduction To Machine Learning Models.- Chapter 5. Model Selection Formachine Learning.- Chapter 6. Multivariate Methods In Machine Learning In The Context Of Biological Data.- Chapter 7. Dimensionality Reduction Methods In Machine Learning.- Chapter 8. Hidden Markov Method.- Chapter 9. Neural Network And Deep Learning. Chapter 10. Ethics In Machine Learning And Artificial Intelligence.- Chapter 11. Machine Learning And Life Sciences.- Chapter 12. Machine Learning And Negleced Tropical Diseases- Chapter 13. Machine Learning In Cardiovascular Diseases.- Chapter 14. Machine Learning And Diabetes.- Chapter 15. Machine Learning And Epilepsy.- Chapter 16. The Microsoft, Google, Facebook, Pytorch And Applications In Biology.- Chapter 17. Applications And Software Of Machine Learning And Ai In Medical Knowledge In Health.- Chapter 18. Cloud Computing Infrastructure In Healthcare Industry.- Chapter 19. Amazon Web Services (Aws) And Microsoft Azure In The Domain Of Life Sciences.- Chapter 20. Toxicity: An Introduction.- Chapter 21. Machine Learning (Ml) And Toxicity Studies. Chapter 22. Applications Of Machine Learning In Study Of Cell Biology.- Chapter 23. Genomics And Machine Learning.- Chapter 24. Cell Fate Analysis And Machine Learning.- Chapter 25. Study Of Biomarker And Machine Learning.- Chapter 26. Animal Behaviour: An Introduction.- Chapter 27. Study Of Animal Behaviour And Machine Learning.- Chapter 28. Machine Learning And Precision Farming.- Chapter 29. Machine Learning In The Study Of Animal Health And Veterinary Sciences.- Chapter 30. Macinelearning And Animalresorviors.- Chapter 31. Challenging Problems In Plant Biology.- Chapter 32. Machine Learning And Plant Sciences. Chapter 33. Machine Learning In Understanding Of Plant Pathogen Interactions.- Chapter 34. Machine Learning In Plant Disease Research.- Chapter 35. Biorobots.- Chapter 36. The Challenges To Application Of Machine Learning In Biological Sciences.- Chapter 37. The Future Of Machine Learning.