Deep Learning in Bioinformatics: Techniques and Applications in Practice
Autor Habib Izadkhahen Limba Engleză Paperback – 19 ian 2022
- Introduces deep learning in an easy-to-understand way
- Presents how deep learning can be utilized for addressing some important problems in bioinformatics
- Presents the state-of-the-art algorithms in deep learning and bioinformatics
- Introduces deep learning libraries in bioinformatics
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (2) | 822.38 lei 5-7 săpt. | |
| ELSEVIER SCIENCE – 19 ian 2022 | 822.38 lei 5-7 săpt. | |
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Specificații
ISBN-13: 9780128238226
ISBN-10: 0128238224
Pagini: 380
Dimensiuni: 191 x 235 mm
Greutate: 0.65 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0128238224
Pagini: 380
Dimensiuni: 191 x 235 mm
Greutate: 0.65 kg
Editura: ELSEVIER SCIENCE
Public țintă
Students, educators, and researchers in the field of bioinformatics, machine learning, biomedical engineering, applied statistics, biostatistics, and computer science Secondary market/audience: Research scientists in medical and biological sciencesCuprins
1. Why Life Science?
2. A Review of Machine Learning
3. An Introduction of Python Ecosystem for Deep Learning
4. Basic Structure of Neural Networks
5. Training Multi-Layer Neural Networks
6. Classification in Bioinformatics
7. Introduction to Deep learning
8. Medical Image Processing: An Insight to Convolutional Neural Networks
9. Popular Deep Learning Image Classifiers
10. Electrocardiogram (ECG) Arrhythmia Classification
11. Autoencoders and Deep Generative Models in Bioinformatics
12. Recurrent Neural Networks: Generating New Molecules and Proteins Sequence Classification
13. Application, Challenge, and Suggestion
2. A Review of Machine Learning
3. An Introduction of Python Ecosystem for Deep Learning
4. Basic Structure of Neural Networks
5. Training Multi-Layer Neural Networks
6. Classification in Bioinformatics
7. Introduction to Deep learning
8. Medical Image Processing: An Insight to Convolutional Neural Networks
9. Popular Deep Learning Image Classifiers
10. Electrocardiogram (ECG) Arrhythmia Classification
11. Autoencoders and Deep Generative Models in Bioinformatics
12. Recurrent Neural Networks: Generating New Molecules and Proteins Sequence Classification
13. Application, Challenge, and Suggestion