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.

Social Network Sites for Scientists: A Quantitative Survey explores the newest social network sites (for example, ResearchGate and Academia.edu) and web bibliographic platforms (Mendeley, Zotero) that have recently emerged for the scholarly community to use in the interchange of information and documents. Chapters describe their main characteristics, what their advantages and limitations are, and the researchers that populate these websites. The surveys included in the book have been conducted following a quantitative approach, and measure the strength of the services provided by the sites in terms of use and activity. In addition, they also discuss the implications of new products in the future of scientific communication and their impact on research activities and evaluation.