Showing posts with label python. Show all posts
Showing posts with label python. Show all posts

Malware Detection (Advances in Information Security) Review

Malware Detection (Advances in Information Security)
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The methods in this book give a view of the state of the art in fighting malware. Written at a strong technical level for people wanting to detect viruses, worms, Trojans and other net dangers. The authors draw together ideas from a wide variety of sources.
One aspect is that the simple idea of virus signatures is under increasing assault from polymorphic viruses that make room within their bodies to insert random bit sequences. Which means that behavioural detection schemes now become more important. In general, these can be much harder to do, than simply finding a signature of a file and then comparing it against a blacklist. This can be done without running the file. A very safe approach. But some behavioural methods might involve having to run the file in a sandbox. Which is more elaborate, and might have the possibility that the binary might do unwanted things or even break out of a badly coded sandbox.
You can consider the text as a good sequel to books that have already been out for a few years, like "Malware: Fighting Malicious Code" by Skoudis and Zeltser, or "Counter Hack Reloaded" by Skoudis and Liston. One obstacle might be the list price of $139. Unfortunately, this is typical of the publisher's offerings, which have a specialised and limited audience. Still, the price can be reasonable, if it exposes you to just one new idea that you would not otherwise have encountered.

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Information Retrieval: Algorithms and Heuristics (The Springer International Series in Engineering and Computer Science) Review

Information Retrieval: Algorithms and Heuristics (The Springer International Series in Engineering and Computer Science)
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If you're working in the IR industry, or want to develop software in this field, this book is a great starting point. A clarification: this will is not a book for researchers -- instead think of it as a book for advanced practitioners or engineers needing to work in this area. Inside you'll see complete worked examples of several fundamental computations rather than detailed proofs.

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Information Retrieval: Algorithms and Heuristics is acomprehensive introduction to the study of information retrievalcovering both effectiveness and run-time performance. The focus of thepresentation is on algorithms and heuristics used to find documentsrelevant to the user request and to find them fast. Through multipleexamples, the most commonly used algorithms and heuristics needed aretackled. To facilitate understanding and applications, introductionsto and discussions of computational linguistics, natural languageprocessing, probability theory and library and computer science areprovided. While this text focuses on algorithms and not on commercialproduct per se, the basic strategies used by many commercial productsare described. Techniques that can be used to find information on theWeb, as well as in other large information collections, are included.This volume is an invaluable resource for researchers, practitioners,and students working in information retrieval and databases. Forinstructors, a set of Powerpoint slides, including speaker notes, areavailable online from the authors.

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Process Mining: Discovery, Conformance and Enhancement of Business Processes Review

Process Mining: Discovery, Conformance and Enhancement of Business Processes
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This book is a must-read for everyone who is interested in Process Mining. It covers the fundamentals and basics of this emerging discipline, and it gives a comprehensive overview of the state of the art in the field.
The book is written by Wil van der Aalst, who has started Process Mining research at the Technical University in Eindhoven about twelve years ago, and who has since been in the center of the developments around this new technology.
Be aware that this book is not a practical handbook that explains how to do Process Mining on a step-by-step basis. Instead, it provides a comprehensive overview about the field of Process Mining as a whole. Although the book does not shy away from technical details, it is easy to read. It provides a very good introduction but also highlights the challenges and complexity of Process Mining when dealing with real-life processes. If you prefer to skip the formal definitions you can still get a good overview because there are many concrete examples.

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The first to cover this missing link between data mining and process modeling, this book provides real-world techniques for monitoring and analyzing processes in real time. It is a powerful new tool destined to play a key role in business process management.


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Social Network Data Analytics Review

Social Network Data Analytics
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This is a very interesting book for both researchers and practitioners in computer science who work in the area of data mining and want to learn the state-of-the-art in social network data analytics. The book provides good coverage of the subject area by focusing on popular research topics, such as the study of the statistical properties that are apparent in "typical" social networks, the problems of community detection and social influence analysis, the expert-location discovery problem, the privacy issues that arise in the context of social networks, as well as visualization techniques, text mining techniques and social tagging. The emerging area of integrating sensors and social networks is also examined. Each chapter of the book contains numerous bibliographic references that will guide readers who are interested in particular topics to explore these topics in more depth. Overall, I highly recommend this book!

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Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes.

Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book.

This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.


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Financial Modelling in Python (The Wiley Finance Series) Review

Financial Modelling in Python (The Wiley Finance Series)
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I am a quant, dealing with financial modeling on daily basis, and this book is the worst that I have ever used.
I had great expectations because I love python but I was so disappointed.
It is really complicated to use the CD and the explanations are so poor.
The book is mostly full with code without real explanations.
Don't buy this book and don't waste your money. It is very bad.
I dont like to write bad reviews, actually it is my first time but this book is very bad.

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"Fletcher and Gardner have created a comprehensive resource that will be of interest not only to those working in the field of finance, but also to those using numerical methods in other fields such as engineering, physics, and actuarial mathematics. By showing how to combine the high-level elegance, accessibility, and flexibility of Python, with the low-level computational efficiency of C++, in the context of interesting financial modeling problems, they have provided an implementation template which will be useful to others seeking to jointly optimize the use of computational and human resources. They document all the necessary technical details required in order to make external numerical libraries available from within Python, and they contribute a useful library of their own, which will significantly reduce the start-up costs involved in building financial models. This book is a must read for all those with a need to apply numerical methods in the valuation of financial claims."–David Louton, Professor of Finance, Bryant University
This book is directed at both industry practitioners and students interested in designing a pricing and risk management framework for financial derivatives using the Python programming language.
It is a practical book complete with working, tested code that guides the reader through the process of building a flexible, extensible pricing framework in Python. The pricing frameworks' loosely coupled fundamental components have been designed to facilitate the quick development of new models. Concrete applications to real-world pricing problems are also provided.
Topics are introduced gradually, each building on the last. They include basic mathematical algorithms, common algorithms from numerical analysis, trade, market and event data model representations, lattice and simulation based pricing, and model development. The mathematics presented is kept simple and to the point.
The book also provides a host of information on practical technical topics such as C++/Python hybrid development (embedding and extending) and techniques for integrating Python based programs with Microsoft Excel.
The book is accompanied by a CD ROM containing a code library; and a companion website www.wiley.com/go/fletcher_python which will feature code-based updates relating to Python 3.0.

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