| Description |
xvii, 251 p. : ill., tables, charts. |
| Contents |
Introducing Machine Learning for Genomics -- Genomics Data Analysis -- Machine Learning Methods for Genomic Applications -- Deep Learning for Genomics -- Introducing Convolutional Neural Networks for Genomics -- Recurrent Neural Networks in Genomics -- Unsupervised Deep Learning with Autoencoders -- GANs for Improving Models in Genomics -- Building and Tuning Deep Learning Models -- Model Interpretability in Genomics -- Model Deployment and Monitoring -- Challenges, Pitfalls, and Best Practices for Deep Learning in Genomics |
| Summary |
The book covers all of the important deep learning algorithms commonly used by the research community and goes into the details of what they are, how they work, and their practical applications in genomics. The book dedicates an entire section to operationalizing deep learning models, which will provide the necessary hands-on tutorials for researchers and any deep learning practitioners to build, tune, interpret, deploy, evaluate, and monitor deep learning models from genomics big data sets. By the end of this book, you'll have learned about the challenges, best practices, and pitfalls of deep learning for genomics. |
| Subject |
Genomics -- Statistical methods
|
|
Genomics -- Data processing
|
| ISBN |
9781804615447 (pbk.) |
|
