Book Content
chapters • 9h total length
1. Introducing Machine Learning for Genomics
2. Genomics Data Analysis
3. Machine Learning Methods for Genomic Applications
4. Deep Learning for Genomics
5. Introducing Convolutional Neural Networks for Genomics
6. Recurrent Neural Networks in Genomics
7. Unsupervised Deep Learning with Autoencoders
8. GANs for Improving Models in Genomics
9. Building and Tuning Deep Learning Models
10. Model Interpretability in Genomics
11. Model Deployment and Monitoring
12. Challenges, Pitfalls, and Best Practices for Deep Learning in Genomics
