The essential concepts of PyTorch and the foundations of a solid understanding of NLP are made accessible to newcomers to the field with this handy guide for developers and data scientists.
Provides developers and data scientists working in the embedded, desktop and big data/Hadoop environments with practical information and relevant techniques on best practices and use cases in deep learning theory. Original.
Malware Data Science explains how to identify, analyze, and classify large-scale malware using machine learning and data visualization. Security has become a ´´big data´´ problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, you´ll need to know how to think like a data scientist. In Malware Data Science, security data scientist Joshua Saxe introduces machine learning, statistics, social network analysis, and data visualization, and shows you how to apply these methods to malware detection and analysis. You´ll learn how to: - Analyze malware using static analysis - Observe malware behavior using dynamic analysis - Identify adversary groups through shared code analysis - Catch 0-day vulnerabilities by building your own machine learning detector - Measure malware detector accuracy - Identify malware campaigns, trends, and relationships through data visualization Whether you´re a malware analyst looking to add skills to your existing arsenal, or a data scientist interested in attack detection and threat intelligence, Malware Data Science will help you stay ahead of the curve.
All the answers to your data science questions Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the ´´what´´ and the ´´why´´ of data science and covering what it takes to lead and nurture a top-notch team of data scientists. With this book, you´ll learn how to incorporate data science as a strategic function into any business, large or small. Find solutions to your real-life challenges as you uncover the stories and value hidden within data. Learn exactly what data science is and why it´s important Adopt a data-driven mindset as the foundation to success Understand the processes and common roadblocks behind data science Keep your data science program focused on generating business value Nurture a top-quality data science team In non-technical language, Data Science Strategy For Dummies outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value.
Since the first publication of The Mythical Man-Month in 1975, no software engineer´s bookshelf has been complete without it. Many software engineers and computer scientists have claimed to be on their second or third copy of the book. Now, Addison-Wesley is proud to present the 20th anniversary edition-and first revised edition ever-of Fred Brooks´s now legendary collection of essays on the management of computer programming projects. The 20th Anniversary edition is an updated, enhanced re-release of the Brooks classic. Included are all of the existing essays that were originally presented, with the addition of three new essays assessing the current status of software project management. Brooks´s well-known 1986 article, No Silver Bullet, is also included. This 20th Anniversary edition is a major event in computer publishing.
The Art and Science of Analyzing Software Data provides valuable information on analysis techniques often used to derive insight from software data. This book shares best practices in the field generated by leading data scientists, collected from their experience training software engineering students and practitioners to master data science. The book covers topics such as the analysis of security data, code reviews, app stores, log files, and user telemetry, among others. It covers a wide variety of techniques such as co-change analysis, text analysis, topic analysis, and concept analysis, as well as advanced topics such as release planning and generation of source code comments. It includes stories from the trenches from expert data scientists illustrating how to apply data analysis in industry and open source, present results to stakeholders, and drive decisions. Presents best practices, hints, and tips to analyze data and apply tools in data science projects Presents research methods and case studies that have emerged over the past few years to further understanding of software data Shares stories from the trenches of successful data science initiatives in industry