Treffer: Deep learning from scratch : building with Python from first principles

With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. Youll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way.Author Seth Weidman shows you how neural networks work using a first principles approach. Youll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, youll be set up for success on all future deep learning projects.This book provides:Extremely clear and thorough mental modelsaccompanied by working code examples and mathematical explanations for understanding neural networks Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented frameworkWorking implementations and clear-cut explanations of convolutional and recurrent neural networks Implementation of these neural network concepts using the popular PyTorch framework ; Con el resurgimiento de las redes neuronales en la década de 2010, el aprendizaje profundo se ha vuelto esencial para los profesionales del aprendizaje automático e incluso para muchos ingenieros de software. Este libro ofrece una introducción completa para científicos de datos e ingenieros de software con experiencia en aprendizaje automático. Comenzará con los fundamentos del aprendizaje profundo y avanzará rápidamente a los detalles de importantes arquitecturas avanzadas, implementando todo desde cero. El autor Seth Weidman le muestra cómo funcionan las redes neuronales utilizando un enfoque de primeros principios. Aprenderá a aplicar redes neuronales multicapa, redes neuronales convolucionales y redes neuronales recurrentes desde ...