Zhimin Zhang – författare

Cheminformatics with Python provides a ground-up, practical introduction that helps reader make effective use of the software. In four parts, including programming, data, methods, and applications, the book provides a brief introduction to Python language and related scientific computing, cheminformatics, machine learning, and deep learning packages and presents a systematic study of the representation of instrumental data, including molecular structures and common chemical databases. The methods section covers analytical signal processing, multivariate calibration, multivariate resolution, classical machine learning, and deep learning methods. Finally, the application section presents case studies of successful applications of cheminformatics in analytical chemistry, metabolomics, drug discovery, and more.A supporting appendix section and the necessary mathematical, statistical, and information theory-related theories are provided, along with practical tips such as code editors and source code management. Online coding materials on GitHub and an individual Jupyter notebook for each chapter further support practical learning. This book will be a great resource for senior undergraduate students, graduate students, post-docs, and professors primarily in the field of computational and analytical chemistry.Provides an in-depth understanding of the application of deep learning in cheminformatics using Python softwareSimultaneously introduces the basic principles and implementations of deep learning algorithms, demonstrating how to apply deep learning models to chemical data for prediction and classification using PythonDelves into rich case studies and practical project examples to help readers apply what they have learned to real chemical problems and dataAccompanied by an online GitHub repository with relevant Python code for each chapterIncludes an accompanying Jupyter Notebook containing relevant data, methods, and application examples, which can be run directly to get the results

This volume collects papers associated with lectures that were presented at the BAIL 2016 conference, which was held from 14 to 19 August 2016 at Beijing Computational Science Research Center and Tsinghua University in Beijing, China. It showcases the variety and quality of current research into numerical and asymptotic methods for theoretical and practical problems whose solutions involve layer phenomena.The BAIL (Boundary And Interior Layers) conferences, held usually in even-numbered years, bring together mathematicians and engineers/physicists whose research involves layer phenomena, with the aim of promoting interaction betweenthese often-separate disciplines. These layers appear as solutions of singularly perturbed differential equations of various types, and are common in physical problems, most notably in fluid dynamics. This book is of interest for current researchers from mathematics, engineering and physics whose work involves the accurate approximation of solutions of singularly perturbed differential equations; that is, problems whose solutions exhibit boundary and/or interior layers.

This volume collects papers associated with lectures that were presented at the BAIL 2016 conference, which was held from 14 to 19 August 2016 at Beijing Computational Science Research Center and Tsinghua University in Beijing, China. It showcases the variety and quality of current research into numerical and asymptotic methods for theoretical and practical problems whose solutions involve layer phenomena.

The BAIL (Boundary And Interior Layers) conferences, held usually in even-numbered years, bring together mathematicians and engineers/physicists whose research involves layer phenomena, with the aim of promoting interaction between

these often-separate disciplines. These layers appear as solutions of singularly perturbed differential equations of various types, and are common in physical problems, most notably in fluid dynamics.

This book is of interest for current researchers from mathematics, engineering and physics whose work involves the accurate app

roximation of solutions of singularly perturbed differential equations; that is, problems whose solutions exhibit boundary and/or interior layers.