1. Why Life Science? 2. A Review of Machine Learning 3. An Introduction to the Python Ecosystem for Deep Learning 4. Preprocessing Techniques for Bioinformatics Data 5. Foundations of Neural Networks and Deep Learning 6. Convolutional Neural Networks in Biology and Bioinformatics 7. Recurrent Neural Networks: Generating New Molecules and Proteins Sequence Classification 8.

Sequence-Based Analysis and Neural Networks 9. Graph Neural Networks for Bioinformatics 10. Transfer Learning in Bioinformatics: Adapting Pre-Trained Models 11. Pathway-Based Neural Networks for Biological Insights 12. Multi-Omics Integration Using Multi-Input Neural Networks 13. Deep Learning for Genomic and Metabolomics Data Analysis 14. Autoencoders and Deep Generative Models in Bioinformatics 15. Interpretable Neural Networks for Understanding Decisions in Biological Processes 16.

Applications of Deep Learning in Personalized Medicine 17. Ethical Considerations and Challenges in Deep Learning for Bioinformatics.