Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More

This document was uploaded by one of our users. The uploader already confirmed that they had the permission to publish it. If you are author/publisher or own the copyright of this documents, please report to us by using this DMCA report form.

Simply click on the Download Book button.

Yes, Book downloads on Ebookily are 100% Free.

Sometimes the book is free on Amazon As well, so go ahead and hit "Search on Amazon"

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

Author(s): Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande
Edition: 1
Publisher: O’Reilly Media
Year: 2019

Language: English
Commentary: True PDF
Pages: 238
City: Sebastopol, CA
Tags: Machine Learning; Deep Learning; Python; Convolutional Neural Networks; Recurrent Neural Networks; Generative Adversarial Networks; Predictive Models; Regularization; Hyperparameter Tuning; Perceptron; Genomics; Linear Models; Biophysics; DNA; RNA; Proteomics; Healthcare; Variational Autoencoders; Chemistry; Workflow; Diabetes; Biochemistry; DeepChem; Microscopy