Author: Publisher:
ISBN:
Category : Chinese language
Languages : zh-CN
Pages : 154
Book Description
Author: 刘珣
Author: Xun Liu
Author: Yuehua LiuDeep Learning
Author: Ian GoodfellowPublisher: MIT Press
ISBN: 0262337371
Category : Computers
Languages : en
Pages : 801
Book Description
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Author: Xun LiuAn Introduction to Statistical Learning
Author: Gareth JamesPublisher: Springer Nature
ISBN: 3031387473
Category : Mathematics
Languages : en
Pages : 617
Book Description
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Author: Cornelius C. KublerModern Mandarin Chinese Grammar Workbook
Author: Claudia RossPublisher: Psychology Press
ISBN: 0415700116
Category : FOREIGN LANGUAGE STUDY
Languages : en
Pages : 281
Book Description
Modern Mandarin Chinese Grammar Workbook is a book of exercises and language tasks for all learners of Mandarin Chinese. Divided into two sections, the Workbook initially provides exercises based on essential grammatical structures, and moves on to practise everyday functions such as making introductions, apologizing and expressing needs. With a comprehensive answer key at the back to enable students to check on their progress, main features include: exercises graded according to level of difficulty cross-referencing to the related Modern Mandarin Chinese Grammar topical exercises that develop students' vocabulary base. Modern Mandarin Chinese Grammar Workbook is ideal for all learners of Mandarin Chinese, from beginner to intermediate and advanced students. It can be used both independently and alongside the Modern Mandarin Chinese Grammar.
Author: Jerry Schmidt