Introduction to Neural Network Verification, Aws Albarghouthi
Автор: Fehr Hans Название: Introduction to Computational Economics Using Fortran ISBN: 0198850379 ISBN-13(EAN): 9780198850373 Издательство: Oxford Academ Рейтинг: Цена: 46980.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This exercise and solutions manual accompanies the main edition of Introduction to Computational Economics Using Fortran. It enables students of all levels to practice the skills and knowledge needed to conduct economic research using Fortran.
Автор: James Gosling; David S.H. Rosenthal; Michelle J. A Название: The NeWS Book ISBN: 146128175X ISBN-13(EAN): 9781461281757 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book is an introduction to NeWS: the Networked, Extensible, Window System from Sun Microsystems. Should there be another edition of this book, we will discuss some of the new development being done in the user interface tool- kit area on NeWS.
Автор: Tevfik Bultan; Fang Yu; Muath Alkhalaf; Abdulbaki Название: String Analysis for Software Verification and Security ISBN: 3319686682 ISBN-13(EAN): 9783319686684 Издательство: Springer Рейтинг: Цена: 79190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
This book discusses automated string-analysis techniques, focusing particularly on automata-based static string analysis. It covers the following topics: automata-bases string analysis, computing pre and post-conditions of basic string operations using automata, symbolic representation of automata, forward and backward string analysis using symbolic automata representation, constraint-based string analysis, string constraint solvers, relational string analysis, vulnerability detection using string analysis, string abstractions, differential string analysis, and automated sanitization synthesis using string analysis.
String manipulation is a crucial part of modern software systems; for example, it is used extensively in input validation and sanitization and in dynamic code and query generation. The goal of string-analysis techniques and this book is to determine the set of values that string expressions can take during program execution. String analysis can be used to solve many problems in modern software systems that relate to string manipulation, such as: (1) Identifying security vulnerabilities by checking if a security sensitive function can receive an input string that contains an exploit; (2) Identifying possible behaviors of a program by identifying possible values for dynamically generated code; (3) Identifying html generation errors by computing the html code generated by web applications; (4) Identifying the set of queries that are sent to back-end database by analyzing the code that generates the SQL queries; (5) Patching input validation and sanitization functions by automatically synthesizing repairs illustrated in this book.
Like many other program-analysis problems, it is not possible to solve the string analysis problem precisely (i.e., it is not possible to precisely determine the set of string values that can reach a program point). However, one can compute over- or under-approximations of possible string values. If the approximations are precise enough, they can enable developers to demonstrate existence or absence of bugs in string manipulating code. String analysis has been an active research area in the last decade, resulting in a wide variety of string-analysis techniques.
This book will primarily target researchers and professionals working in computer security, software verification, formal methods, software engineering and program analysis. Advanced level students or instructors teaching or studying courses in computer security, software verification or program analysis will find this book useful as a secondary text.
Автор: Bruce F. Torrence, Eve A. Torrence Название: The Student`s Introduction to Mathematica and the Wolfram Language ISBN: 110840636X ISBN-13(EAN): 9781108406369 Издательство: Cambridge Academ Рейтинг: Цена: 51750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book introduces Mathematica (R) and the Wolfram Language (TM) in the context of the standard university mathematics curriculum. It equips current and former students to harness these tools to explore ideas from pre-calculus, calculus, and linear algebra. Additional chapters on programming and 3D printing provide outlets for further exploration.
Автор: Gerard Marshall Raj; Ramasamy Raveendran Название: Introduction to Basics of Pharmacology and Toxicology: Volume 1: General and Molecular Pharmacology: Principles of Drug Action ISBN: 9813297786 ISBN-13(EAN): 9789813297784 Издательство: Springer Рейтинг: Цена: 149060.00 T Наличие на складе: Нет в наличии. Описание: This book illustrates, in a comprehensive manner, the most crucial principles involved in pharmacology and allied sciences. The title begins by discussing the historical aspects of drug discovery, with up to date knowledge on Nobel Laureates in pharmacology and their significant discoveries. It then examines the general pharmacological principles - pharmacokinetics and pharmacodynamics, with in-depth information on drug transporters and interactions. In the remaining chapters, the book covers a definitive collection of topics containing essential information on the basic principles of pharmacology and how they are employed for the treatment of diseases.Readers will learn about special topics in pharmacology that are hard to find elsewhere, including issues related to environmental toxicology and the latest information on drug poisoning and treatment, analytical toxicology, toxicovigilance, and the use of molecular biology techniques in pharmacology. The book offers a valuable resource for researchers in the fields of pharmacology and toxicology, as well as students pursuing a degree in or with an interest in pharmacology.
Автор: The International Neural Society(INNS), The IEEE N Название: INNC 90 PARIS ISBN: 079230831X ISBN-13(EAN): 9780792308317 Издательство: Springer Рейтинг: Цена: 81050.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: International Neural Network Conference, July 9-13, 1990, Palais des Congres, Paris, France
Автор: Igor Livshin Название: Artificial Neural Networks with Java ISBN: 1484244206 ISBN-13(EAN): 9781484244203 Издательство: Springer Рейтинг: Цена: 32600.00 T Наличие на складе: Нет в наличии. Описание:
Use Java to develop neural network applications in this practical book. After learning the rules involved in neural network processing, you will manually process the first neural network example. This covers the internals of front and back propagation, and facilitates the understanding of the main principles of neural network processing. Artificial Neural Networks with Java also teaches you how to prepare the data to be used in neural network development and suggests various techniques of data preparation for many unconventional tasks.
The next big topic discussed in the book is using Java for neural network processing. You will use the Encog Java framework and discover how to do rapid development with Encog, allowing you to create large-scale neural network applications.
The book also discusses the inability of neural networks to approximate complex non-continuous functions, and it introduces the micro-batch method that solves this issue. The step-by-step approach includes plenty of examples, diagrams, and screen shots to help you grasp the concepts quickly and easily.
What You Will Learn
Prepare your data for many different tasksCarry out some unusual neural network tasksCreate neural network to process non-continuous functionsSelect and improve the development model
Who This Book Is For
Intermediate machine learning and deep learning developers who are interested in switching to Java.
Автор: Singh Ghotra Manpreet, Dua Rajdeep Название: Neural Network Programming with TensorFlow ISBN: 1788390393 ISBN-13(EAN): 9781788390392 Издательство: Неизвестно Рейтинг: Цена: 53940.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Moocarme Matthew, Abdolahnejad Mahla, Bhagwat Ritesh Название: The Deep Learning with Keras Workshop: Learn how to define and train neural network models with just a few lines of code ISBN: 1800562969 ISBN-13(EAN): 9781800562967 Издательство: Неизвестно Рейтинг: Цена: 39410.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The Deep Learning with Keras Workshop outlines a simple and straightforward way for you to understand deep learning with Keras. Starting with basic concepts such as data preprocessing, this book equips you with all the tools and techniques required for training your neural networks to solve various modeling problems.
Автор: Pajankar Ashwin, Joshi Aditya Название: Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch ISBN: 1484279204 ISBN-13(EAN): 9781484279205 Издательство: Springer Рейтинг: Цена: 55890.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Chapter 1: Getting Started with Python 3 and Jupyter NotebookChapter Goal: Introduce the reader to the basics of Python Programming language, philosophy, and installation. We will also learn how to install it on various platforms. This chapter also introduces the readers to Python programming with Jupyter Notebook. In the end, we will also have a brief overview of the constituent libraries of sciPy stack.No of pages - 30Sub -Topics1. Introduction to the Python programming language2. History of Python3. Python enhancement proposals (PEPs)4. Philosophy of Python5. Real life applications of Python6. Installing Python on various platforms (Windows and Debian Linux Flavors)7. Python modes (Interactive and Script)8. Pip (pip installs python)9. Introduction to the scientific Python ecosystem10. Overview of Jupyter Notebook11. Installation of Jupyter Notebook12. Running code in Jupyter Notebook Chapter 2: Getting Started with NumPyChapter Goal: Get started with NumPy Ndarrays and the basics of NumPy library. The chapter covers the instructions for installation and basic usage of NumPy.No of pages: 10Sub - Topics: 1. Introduction to NumPy2. Install NumPy with pip33. Indexing and Slicing of ndarrays4. Properties of ndarrays5. Constants in NumPy6. Datatypes in datatypes Chapter 3: Introduction to Data VisualizationChapter goal - In this chapter, we will discuss the various ndarray creation routines available in NumPy. We will also get started with Visualizations with Matplotlib. We will learn how to visualize the various numerical ranges with Matplotlib.No of pages: 15Sub - Topics: 1. Ones and zeros2. Matrices3. Introduction to Matplotlib4. Running Matplotlib programs in Jupyter Notebook and the script mode5. Numerical ranges and visualizations Chapter 4: Introduction to Pandas Chapter goal - Get started with Pandas data structuresNo of pages: 10Sub - Topics: 1. Install Pandas2. What is Pandas3. Introduction to series4. Introduction to dataframesa) Plain Text Fileb) CSVc) Handling excel filed) NumPy file formate) NumPy CSV file readingf) Matplotlib Cbookg) Read CSVh) Read Exceli) Read JSONj) Picklek) Pandas and webl) Read SQLm) Clipboard Chapter 5: Introduction to Machine Learning with Scikit-LearnChapter goal - Get acquainted with machine learning basics and scikit-Learn libraryNo of pages: 101. What is machine learning, offline and online processes2. Supervised/unsupervised methods3. Overview of scikit learn library, APIs4. Dataset loading, generated datasets Chapter 6: Preparing Data for Machine LearningChapter Goal: Clean, vectorize and transform dataNo of Pages: 151. Type of data variables2. Vectorization3. Normalization4. Processing text and images Chapter 7: Supervised Learning Methods - 1Chapter Goal: Learn and implement classification and regression algorithmsNo of Pages: 301. Regression and classification, multiclass, multilabel classification2. K-nearest neighbors3. Linear regression, understanding parameters4. Logistic regression5. Decision trees Chapter 8: Tuning Supervised L
Автор: Gridin Название: Automated Deep Learning Using Neural Network Intelligence ISBN: 1484281489 ISBN-13(EAN): 9781484281482 Издательство: Springer Рейтинг: Цена: 60550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Optimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural network design are presented. The book teaches you how to construct a search space and launch an architecture search using the latest state-of-the-art exploration strategies: Efficient Neural Architecture Search (ENAS) and Differential Architectural Search (DARTS). You will learn how to automate the construction of a neural network architecture for a particular problem and dataset. The book focuses on model compression and feature engineering methods that are essential in automated deep learning. It also includes performance techniques that allow the creation of large-scale distributive training platforms using NNI. After reading this book, you will know how to use the full toolkit of automated deep learning methods. The techniques and practical examples presented in this book will allow you to bring your neural network routines to a higher level. What You Will Learn * Know the basic concepts of optimization tuners, search space, and trials * Apply different hyper-parameter optimization algorithms to develop effective neural networks * Construct new deep learning models from scratch * Execute the automated Neural Architecture Search to create state-of-the-art deep learning models * Compress the model to eliminate unnecessary deep learning layers Who This Book Is For Intermediate to advanced data scientists and machine learning engineers involved in deep learning and practical neural network development
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