Deep Learning from Scratch: Building with Python from First Principles

I. THÔNG TIN SẢN PHẨM
Mã sản phẩm : STT609
Nhà xuất bản:  O'Reilly Media; 1st edition (October 1, 2019)
Tác giả :  Seth Weidman
Ngôn Ngữ : Tiếng Anh
ISBN      : 935213902X
Số trang    : 252 pages
Hình thức : Bìa Mềm, IN ĐEN TRẮNG
Loại : Sách gia công đóng gáy keo chắc chắn chất lượng cao
Giấy in : Giấy ngoại định lượng 70msg, viết vẽ và hightlight thoải mái.
Chất lượng : Bản in rõ nét, giá rất tốt cho mọi người.

II. MÔ TẢ SẢN PHẨM
1.Mô tả sản phẩm
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

        
2. Tác giả
Seth Weidman is a data scientist who has applied and taught machine learning concepts for several years. He started out as the first data scientist at Trunk Club, where he built lead scoring models and recommender systems, and currently works at Facebook, where he builds machine learning models for their infrastructure team. In between he taught data science and machine learning for the bootcamps and on the corporate training team at Metis. He is passionate about explaining complex concepts simply, striving to find the simplicity on the other side of complexity.