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. You'll 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. You'll 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, you'll be set up for success on all future deep learning projects. This book provides: Extremely clear and thorough mental models--accompanied 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 framework Working implementations and clear-cut explanations of convolutional and recurrent neural networks Implementation of these neural network concepts using the popular PyTorch framework

随着2010年代神经网络的重新兴起，深度学习已经成为机器学习从业者甚至许多软件工程师的必备工具。本书为具有机器学习经验的数据科学家和软件工程师提供了一个全面的介绍。你将从深度学习的基础知识开始，迅速进入重要的高级架构的细节，沿途从头实现一切。作者Seth Weidman用第一原理的方法告诉你神经网络是如何工作的。你将学会如何从头开始应用多层神经网络、卷积神经网络和递归神经网络。通过对神经网络在数学上、计算上和概念上的工作原理的透彻理解，你将为未来所有深度学习项目的成功做好准备。本书提供。极其清晰和透彻的心理模型--伴随着工作代码实例和数学解释--用于理解神经网络 使用易于理解的面向对象框架，从头开始实现多层神经网络 卷积和递归神经网络的工作实现和清晰的解释 使用流行的PyTorch框架实现这些神经网络概念