Dive into Deep Learning, 1st Edition by Aston Zhang (.PDF)
File Size: 42.6 MB
Dive into Deep Learning, 1st Edition (December 7, 2023) by Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola
Requirements: .PDF reader, 42.6 MB | True PDF
Overview: Deep Learning has revolutionized pattern recognition, introducing tools that power a wide range of technologies in such diverse fields as computer vision, natural language processing, and automatic speech recognition. Applying Deep Learning requires you to simultaneously understand how to cast a problem, the basic mathematics of modeling, the algorithms for fitting your models to data, and the engineering techniques to implement it all. This book is a comprehensive resource that makes Deep Learning approachable, while still providing sufficient technical depth to enable engineers, scientists, and students to use Deep Learning in their own work. No previous background in Machine Learning or Deep Learning is required―every concept is explained from scratch and the appendix provides a refresher on the mathematics needed. Runnable code is featured throughout, allowing you to develop your own intuition by putting key ideas into practice. This book teaches Deep Learning concepts from scratch. Sometimes, we delve into fine details about models that would typically be hidden from users by modern Deep Learning frameworks. This comes up especially in the basic tutorials, where we want you to understand everything that happens in a given layer or optimizer. In these cases, we often present two versions of the example: one where we implement everything from scratch, relying only on NumPy-like functionality and automatic differentiation, and a more practical example, where we write succinct code using the high-level APIs of Deep Learning frameworks. Most of the code in this book is based on PyTorch, a popular open-source framework that has been enthusiastically embraced by the Deep Learning research community. This book is for students (undergraduate or graduate), engineers, and researchers, who seek a solid grasp of the practical techniques of Deep Learning. Because we explain every concept from scratch, no previous background in Deep Learning or Machine Learning is required. Fully explaining the methods of Deep Learning requires some mathematics and programming, but we will only assume that you enter with some basics, including modest amounts of linear algebra, calculus, probability, and Python programming.
Genre: Non-Fiction > Tech & Devices
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