With much success already attributed to deep learning, this discipline has started making waves throughout science broadly and the life sciences in particular. With this practical book, developers and scientists will learn how deep learning is used for genomics, chemistry, biophysics, microscopy, medical analysis, drug discovery, and other fields. As a running case study, the authors focus on the problem of designing new therapeutics, one of science's greatest challenges because this practice ties together physics, chemistry, biology and medicine. Using TensorFlow and the DeepChem library, this book introduces deep network primitives including image convolutional networks, 1D convolutions for genomics, graph convolutions for molecular graphs, atomic convolutions for molecular structures, and molecular autoencoders. Deep Learning for the Life Sciences is ideal for practicing developers interested in applying their skills to scientific applications such as biology, genetics, and drug discovery, as well as scientists interested in adding deep learning to their core skills.