Introduction to Deep Learning and Neural Networks with Python (TM): A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and Python (TM) code examples to clarify neural network calculations, by book's end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and Python (TM) examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network.

This updated edition is designed as a brief introduction to the fundamental particles that make up the matter in our universe. Numerous examples, figures, and simple explanations enable general readers and physics students to understand complex concepts related to the universe. Selected topics include atoms, quarks, accelerators, detectors, colliders, string theory, and more.