Introduction to Deep Learning and Neural Networks with PythonT: A Practical Guide

Introduction to Deep Learning and Neural Networks with PythonT: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and PythonT 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 PythonT 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 networksProvides a problem-based approach to building artificial neural networks using real dataDescribes PythonT functions and features for neuroscientistsUses a careful tutorial approach to describe implementation of neural networks in PythonTFeatures math and code examples (via companion website) with helpful instructions for easy implementation INDICE: 1. Preparing the Development Environment2. Introduction to ANN3. ANN with 1 Input and 1 Output4. Working with Any Number of Inputs5. Working with Hidden Layers6. Using Any Number of Hidden Neurons7. ANN with 2 Hidden Layers8. ANN with 3 Hidden Layers9. Any Number of Hidden Layers10. Generic ANN11. Speeding Neural Network using Cython and PyPy12. Deploying Neural Network to Mobile Devices

• ISBN: 9780323909334
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 200
• Fecha Publicación: 01/12/2020
• Nº Volúmenes: 1
• Idioma: Inglés