pdf | 15.72 MB | English|
Isbn:1119861861 |
Author: Ahmed Fawzy Gad, Fatima Ezzahra Jarmouni |
Year: 2024
Description:
Introduction to Deep Learning and Neural Networks with Python™: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and Python™ 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™ 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. - Examines the practical side of deep learning and neural networks - Provides a problem-based approach to building artificial neural networks using real data - Describes Python™ functions and features for neuroscientists - Uses a careful tutorial approach to describe implementation of neural networks in Python™ - Features math and code examples (via companion website) with helpful instructions for easy implementation
Category:Medicine & Nursing, Science & Technology, Medicine, Biology & Life Sciences, Basic Sciences, Neuroscience
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