By closing the gap between general programming books and those on laboratory automation, this timely book makes accessible to every laboratory technician or scientist what has traditionally been restricted to highly specialized professionals.
By closing the gap between general programming books and those on laboratory automation, this timely book makes accessible to every laboratory technician or scientist what has traditionally been restricted to highly specialized professionals.
This first introductory book designed to train novice programmers is based on a student course taught by the author, and has been optimized for biology students without previous experience in programming.
Das Buch stellt die verschiedenen Elemente der Programmiersprache C (vor allem im Rahmen des Sprachstandards ANSI-C / C99) dar, wobei die Inhalte einerseits thematisch sortiert werden, andererseits aber auch versucht wird, einen „arbeitsfähigen“ Weg zu generieren, so dass bereits zu einem frühen Lesezeitpunkt das begleitende praktische Arbeiten (Programmieren) möglich ist.
Insbesondere die praktische Informatik lebt vom Ausprobieren verschiedener Lösungswege, dem Experimentieren mit Programmkonstrukten und Algorithmen, und allgemein vom "Selbermachen".
Begin your generative AI journey with Python as you explore large language models, understand responsible generative AI practices, and apply your knowledge to real-world applications through guided tutorialsKey FeaturesGain expertise in prompt engineering, LLM fine-tuning, and domain adaptationUse transformers-based LLMs and diffusion models to implement AI applicationsDiscover strategies to optimize model performance, address ethical considerations, and build trust in AI systemsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application.
This book explains the field of Generative Artificial Intelligence (AI), focusing on its potential and applications, and aims to provide you with an understanding of the underlying principles, techniques, and practical use cases of Generative AI models.
This book is an excellent, helpful and up-to-date resource for all candidates preparing for the ISTQB Foundation Level certification exam based on the new Foundation Level 2018 Syllabus.
This book focuses on social network analysis from a computational perspective, introducing readers to the fundamental aspects of network theory by discussing the various metrics used to measure the social network.
This easy-to-understand textbook presents a modern approach to learning numerical methods (or scientific computing), with a unique focus on the modeling and applications of the mathematical content.
Software maintenance work is often considered a dauntingly rigid activity - this book proves the opposite: it demands high levels of creativity and thinking outside the box.
This book presents an introduction to Evolutionary Game Theory (EGT) which is an emerging field in the area of complex systems attracting the attention of researchers from disparate scientific communities.
This book provides an account of the use of computational tactical metrics in improving sports analysis, in particular the use of Global Positioning System (GPS) data in soccer.
Computational Literacy for the Humanities provides an introduction to mathematics and programming that is specifically designed for use by those engaged in the humanities.
While other textbooks devote their pages to explaining introductory programming concepts, The Python Workbook focuses exclusively on exercises, following the philosophy that computer programming is a skill best learned through experience and practice.
The goal of this book is to gather in a single work the most relevant concepts related in optimization methods, showing how such theories and methods can be addressed using the open source, multi-platform R tool.
While other textbooks devote their pages to explaining introductory programming concepts, The Python Workbook focuses exclusively on exercises, following the philosophy that computer programming is a skill best learned through experience and practice.
This textbook presents methods and techniques for time series analysis and forecasting and shows how to use Python to implement them and solve data science problems.
