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

3.95  ·  Rating details ·  21 ratings  ·  5 reviews
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 read
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ebook, 238 pages
Published April 10th 2019 by O'Reilly Media
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James Warren
This book has a distinctive "color by numbers" feel. It provides highly useful background information, but the provided examples have limited illustrative value. Reading this book is a nice overview, but it certainly will not prepare you to conduct similar analyses independently. ...more
Will Bowers
While this is an interesting intro to multiple areas of deep learning across bioinformatics, cheminformatics, and genomics, it has some flaws in that deepchem, the python package this book revolves around, needs some very specific versions of python, tensorflow, scikit learn, and more. It's common to be overwhelmed with warning labels about deprecations, or simply things not working.

While yes, it is possible to make a separate environment and run the book exercises there, it does raise complicat
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Elmer Peramo
This is more like a survey paper than a cohesive textbook. There are some interesting chapters, but others are lacking in depth and detail. It's better for experienced deep learning practitioners who want to learn domains in chemistry, biophysics, and genomics than for biologists who need to study deep learning. ...more
Radu Angelescu
Nice

It's a nice book. I would have liked it to be more in depth but I guess it's meant to be for casual readers.
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Richard
Jun 25, 2019 rated it it was amazing
I was looking for a summary of how life science data is represented in machine learning experiments. This is perfect for what I needed.
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