Deep Learning for the Life Sciences
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
About the Author
Bharath Ramsundar is the co-founder and CTO of Computable, a blockchain company working to build a decentralized data marketplace for AI applications. Bharath is also the lead developer and creator of DeepChem.io, an open source package founded on Tensorflow that aims to democratize the use of deep-learning in drug-discovery, and the co-creator of the moleculenet.ai benchmark suite.
Bharath Ramsundar received a BA and BS from UC Berkeley in EECS and Mathematics and was valedictorian of his graduating class in mathematics. He recently finished his PhD in computer science at Stanford University (all but dissertation) with the Pande group, supported by a Hertz Fellowship, the most selective graduate fellowship in the sciences.
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