Peter Eastman

Dogs Fred and Ted pack up their cars and go on a road trip, and their different ways of doing things have interesting results.

Fred and Ted's road trip

Fred and Ted—beloved canine stars of P. D. Eastman’s Big Dog . . . Little Dog—are back in an all-new Beginner Book written and illustrated by P. D.’s son, Peter Eastman! In this story Fred and Ted go camping, and as usual, their uniquely different approaches to doing things (such as packing equipment, setting up camp, and fishing techniques) have humorous—and sometimes surprising—results. A charming introduction to opposites that beginner readers will find ruff to put down! Originally created by Dr. Seuss, Beginner Books encourage children to read all by themselves, with simple words and illustrations that give clues to their meaning.

Fred and Ted Go Camping

Patrick Walters

Bharath Ramsundar

Vijay Pande

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.

Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More

Deep Learning for the Life Sciences

Fred and Ted—beloved canine stars of P.D. Eastman's Big Dog . . . Little Dog and son Peter Eastman's Fred and Ted Go Camping and Fred and Ted Like to Fly—are on the move once again in Fred and Ted's Road Trip, the 100th Beginner Book published since Dr. Seuss launched the series in 1957 with The Cat in the Hat. In their latest adventure, Fred and Ted pack a picnic basket, jump in their cars, and hit the open road—but as usual, things don't go as the doggy duo plan. They encounter muddy roads, thunder and lightning, tire-piercing cacti, and overenthusiastic tire inflating that almost sends Fred into orbit! Perfect for P. D. Eastman fans, dog lovers, and families on car trips, this is a beginner reader that harkens back to the best of the Beginner Books edited by the Good Doctor himself. Originally created by Dr. Seuss, Beginner Books encourage children to read all by themselves, with simple words and illustrations that give clues to their meaning.

Fred and Ted's Road Trip

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

Peter Eastman

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