Deep learning has already achieved remarkable results in many fields. Now it's making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields.
Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You'll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine--an example that represents one of science's greatest challenges.
Learn the basics of performing machine learning on molecular data
Understand why deep learning is a powerful tool for genetics and genomics
Apply deep learning to understand biophysical systems
Get a brief introduction to machine learning with DeepChem
Use deep learning to analyze microscopic images
Analyze medical scans using deep learning techniques
Learn about variational autoencoders and generative adversarial networks
Interpret what your model is doing and how it's working