Advanced Guide to Python 3 Programming

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Advanced Guide to Python 3 ProgrammingAdvanced Guide to Python 3 Programming (Undergraduate Topics in Computer Science)
By 作者: John Hunt
ISBN-10 书号: 3030259420
ISBN-13 书号: 9783030259426
Edition 版本: 1st ed. 2019
Release Finelybook 出版日期: 2019-09-18
pages 页数: (497 )

$69.99

Book Description to Finelybook sorting

Advanced Guide to Python 3 Programming delves deeply into a host of subjects that you need to understand if you are to develop sophisticated real-world programs. Each topic is preceded by an introduction followed by more advanced topics, along with numerous examples, that take you to an advanced level.
There are nine different sections within the book covering Computer Graphics (including GUIs), Games, Testing, File Input and Output, Databases Access, Logging, Concurrency and Parallelism, Reactive programming, and Networking. Each section is self-contained and can either be read on its own or as part of the book as a whole.
This book is aimed at the those who have learnt the basics of the Python 3 language but want to delve deeper into Python’s eco system of additional libraries and modules, to explore concurrency and parallelism, to create impressive looking graphical interfaces, to work with databases and files and to provide professional logging facilities.

Contents


1Introduction
Computer Graphics
2Introduction to Computer Graphics
3Python Turtle Graphics
4Computer Generated Art
5Introduction to Matplotlib
6Graphing with Matplotib pyplot
7 Graphical User Interfaces
8The wxPython GUI Library
9 Events in wxPython User Interfaces
10PyDraw wxPython Example Application
Computer Games
11 Introduction to Games Programming
12 Building Games with pygame
13 StarshipMeteors pygame
Testing
14 Introduction to Testing
15PyTest Testing Framework
16Mocking for Testing
File Input/Output
17 Introduction to Files,Paths and lO
18Reading and Writing Files
19 Stream lO
20Working with CSV Files
21Working with Excel Files
22 Regular Expressions in Python
Database Access
23 Introduction to Databases
24Python DB-API
25PyMySQL Module
Logging
26Introduction to Logging
27 Logging in Python
28Advanced Logging
Concurrency and Parallelism
29Introduction to Concurrency and Parallelism
30 Threading
31 Multiprocessing
32 Inter Thread/Process Synchronisation
33 Futures
34 Concurrency with AsynclO
Reactive Programming
35Reactive Programming Introduction
36RxPy Observables,Observers and Subjects
37 RxPy Operators
Network Programming
38 Introduction to Sockets and Web Services
39 Sockets in Python
40 Web Services in Python
41 Bookshop Web Service

Adversarial Machine Learning

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Adversarial Machine Learning
Adversarial Machine Learning
By 作者: Anthony D. Joseph – Blaine Nelson – Benjamin I. P. Rubinstein – J. D. Tygar
ISBN-10 书号: 1107043468
ISBN-13 书号: 9781107043466
Edition 版本: 1
Release Finelybook 出版日期: 2019-04-11
pages 页数: (338 )

$79.99

Book Description to Finelybook sorting

Written by leading researchers, this complete introduction brings together all the theory and tools needed for building robust machine learning in adversarial environments. Discover how machine learning systems can adapt when an adversary actively poisons data to manipulate statistical inference, learn the latest practical techniques for investigating system security and performing robust data analysis, and gain insight into new approaches for designing effective countermeasures against the latest wave of cyber-attacks. Privacy-preserving mechanisms and the near-optimal evasion of classifiers are discussed in detail, and in-depth case studies on email spam and network security highlight successful attacks on traditional machine learning algorithms. Providing a thorough overview of the current state of the art in the field, and possible future directions, this groundbreaking work is essential reading for researchers, practitioners and students in computer security and machine learning, and those wanting to learn about the next stage of the cybersecurity arms race.


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Concurrency with Modern C++: What every professional C++ programmer should know about concurrency

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Concurrency with Modern C++
Concurrency with Modern C++: What every professional C++ programmer should know about concurrency
By 作者: Rainer Grimm
Pub Date: 2019
ISBN: n/a
Pages: 475
Language: English
Format: PDF
Size: 10 Mb


Book Description to Finelybook sorting

C++11 is the first C++ standard that deals with concurrency. The story goes on with C++17 and will continue with C++20/23.
I’ll give you a detailed insight into the current and the upcoming concurrency in C++. This insight includes the theory and a lot of practice.
Concurrency with Modern C++ is a journey through current and upcoming concurrency in C++.

C++11 and C++14 have the basic building blocks for creating concurrent or parallel programs.
With C++17 we got the parallel algorithms of the Standard Template Library (STL). That means, most of the algorithms of the STL can be executed sequential, parallel, or vectorized.
The concurrency story in C++ goes on. With C++20/23 we can hope for executors, extended futures, coroutines, transactions, and more.
This book explains you the details to concurrency in modern C++ and gives you, in addition, more than 100 running code examples . Therefore you can combine the theory with the practices and get the most of it.
Because this book is about concurrency, I present a lot of pitfalls and show you how to overcome them.


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Foundations of Deep Reinforcement Learning: Theory and Practice in Python

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Foundations of Deep Reinforcement Learning: Theory and Practice in Python
Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
By 作者: Laura Graesser – Wah Loon Keng
ISBN-10 书号: 0135172381
ISBN-13 书号: 9780135172384
Edition 版本: 1
Release Finelybook 出版日期: 2019-12-15
pages 页数: (416 )

$49.99

Book Description to Finelybook sorting

The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice
Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games—such as Go, Atari games, and DotA 2—to robotics.
Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work.

Understand each key aspect of a deep RL problem
Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER)
Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO)
Understand how algorithms can be parallelized synchronously and asynchronously
Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work
Explore algorithm benchmark results with tuned hyperparameters
Understand how deep RL environments are designed
This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python.


Contents


Preface
Acknowledgements
About the Authors
Chapter 1. Introduction
1.1Reinforcement Learning
1.2Reinforcement Learning as MDP
1.3 Learnable Functions in Reinforcement Learning
1.4Deep Reinforcement Learning Algorithms
1.5Deep Learning for Reinforcement Learning
1.6Reinforcement Learning and Supervised Learning
1.7 Summary
Part l: Policy-based & Value-based Algorithms
Chapter 2. Reinforce
Chapter 3. SARSA
Chapter 4. Deep Q-Networks(DQN)
Chapter 5. Improving DQN
Part l: Combined methods
Chapter 6. Advantage Actor-Critic(A2C)
Chapter 7. Proximal Policy Optimization(PPO)
Chapter 8. Parallelization Methods
Chapter 9. Algorithm Summary
Part ll: Practical Tips
Chapter 10. Getting Deep RL to Work
Chapter 11. SLM Lab
Chapter 12. Network architectures
Chapter 13. Hardware
Chapter 14. Environment Design
Epilogue
Appendix A. Deep Reinforcement Learning Timeline
Appendix B. Example Environments
Bibliography


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Foundations of Deep Reinforcement Learning 9780135172384.zip

Soft Skills to Advance Your Developer Career: Actionable Steps to Help Maximize Your Potential

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Soft Skills to Advance Your Developer Career: Actionable Steps to Help Maximize Your Potential
Soft Skills to Advance Your Developer Career: Actionable Steps to Help Maximize Your Potential
By 作者: Zsolt Nagy
ISBN-10 书号: 1484250915
ISBN-13 书号: 9781484250914
Edition 版本: 1st ed.
Release Finelybook 出版日期: 2019-09-21
pages 页数: (296 )

$37.99

Book Description to Finelybook sorting

As a software developer, your technical skill set is in high demand. Devices and technology have become an integral part of our everyday lives and no digital organization can thrive without technical professionals on the payroll. However, career plateaus are inevitable in even the most high-demand field. Companies do not only need software developers; they need software developers with soft skills.
In Soft Skills to Advance Your Developer Career, author Zsolt Nagy explores how emotional intelligence can give your software development career an edge. These subjects are not taught in school, and unfortunately the career advancement of many excellent developers can be blocked by their inability to effectively communicate their needs, assert themselves, and negotiate confidently. Throughout this book, Nagy shows you how to actively improve and prioritize your soft skills so that you can better represent the holistic interests of your team, obtain better working conditions, negotiate raises, and increase your variety of employment options by elevating your interviewing skills.
Discover the obstacles standing between you and a fulfilling career by finding and improving strengths you may not have even known you had. Jump out of your box with Soft Skills to Advance Your Developer Career and leverage your expertise with effortless confidence at all stages of your professional journey.
What You Will Learn

Examine why software developer careers cannot be treated similarly as any other career path
Understand the four soft-skills you need to advance your career
Develop a strategy for your personal brand and align it with your career plan
Realize the role of assertive communication, and the importance of giving and receiving feedback
Create a plan for setting yourself up for a raise or promotion
Discover techniques for acing the behavioral and coding interview
1.The Importance of Soft Skills
2.Mindset for Career Advancement and Life
3.Discover Your Individual Goals
4.Your Online and Offline Presence
5.Set Yourself Up for a Promotion
6.Negotiating Raises and Promotions
7.Get Your Dream Job
8.Your Future Is in Your Hands
Back Matter

Algorithms of the Intelligent Web 2ed Edition

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Algorithms of the Intelligent Web 2ed Edition
Algorithms of the Intelligent Web
By 作者: Douglas McIlwraith – Haralambos Marmanis – Dmitry Babenko
ISBN-10 书号: 1617292583
ISBN-13 书号: 9781617292583
Edition 版本: 2
Release Finelybook 出版日期: 2016-09-08
pages 页数: (240 )

$44.99


Book Description to Finelybook sorting

Algorithms of the Intelligent Web, Second Edition teaches the most important approaches to algorithmic web data analysis, enabling you to create your own machine learning applications that crunch, munge, and wrangle data collected from users, web applications, sensors and website logs.
Valuable insights are buried in the tracks web users leave as they navigate pages and applications. You can uncover them by using intelligent algorithms like the ones that have earned Facebook, Google, and Twitter a place among the giants of web data pattern extraction.
Algorithms of the Intelligent Web, Second Edition teaches you how to create machine learning applications that crunch and wrangle data collected from users, web applications, and website logs. In this totally revised edition, you’ll look at intelligent algorithms that extract real value from data. Key machine learning concepts are explained with code examples in Python’s scikit-learn. This book guides you through algorithms to capture, store, and structure data streams coming from the web. You’ll explore recommendation engines and dive into classification via statistical algorithms, neural networks, and deep learning.
What’s Inside

Introduction to machine learning
Extracting structure from data
Deep learning and neural networks
How recommendation engines work
Contents
Foreword
Preface
Acknowledgments
About this Book
Chapter 1.Building applications for the intelligent web
Chapter 2.Extracting structure from data:clustering and transforming your data
Chapter 3.Recommending relevant content
Chapter 4.Classification:placing things where they belong
Chapter 5.Case study:click prediction for online advertising
Chapter 6.Deep learning and neural networks
Chapter 7.Making the right choice
Chapter 8.The future of the intelligent web
Appendix.Capturing data on the web
Index
List of Figures
List of Tables
List of Listings

Mastering Autodesk Revit 2018

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Mastering Autodesk Revit 2018
Mastering Autodesk Revit 2018
By 作者: Marcus Kim – Lance Kirby – Eddy Krygiel
ISBN-10 书号: 1119386721
ISBN-13 书号: 9781119386728
Edition 版本: 1
Release Finelybook 出版日期: 2017-07-17
pages 页数: 1056

$70

Book Description to Finelybook sorting

The best-selling Revit guide, now more complete than ever with all-new coverage on the 2018 release
Mastering Autodesk Revit 2018 for Architecture is packed with focused discussions, detailed exercises, and real-world examples to help you get up to speed quickly on the latest version of Autodesk Revit for Architecture. Organized according to how you learn and implement the software, this book provides expert guidance for all skill levels. Hands-on tutorials allow you to dive right in and start accomplishing vital tasks, while compelling examples illustrate how Revit for Architecture is used in every project. The companion website features before-and-after tutorial files, additional advanced content, and an hour of video on crucial techniques to help you quickly master this powerful software. From basic interface topics to advanced visualization techniques and documentation, this invaluable guide is your ideal companion through the Revit Architecture workflow.
Whether you’re preparing for Autodesk certification exams or just want to become more productive with the architectural design software, practical exercises and expert instruction will get you where you need to be.
Understand key BIM and Revit concepts and master the Revit interface
Delve into templates, work-sharing, and managing Revit projects
Master modeling and massing, the Family Editor, and visualization techniques
Explore documentation, including annotation, detailing, and complex structures
BIM software has become a mandatory asset in today’s architecture field; automated documentation updates reduce errors while saving time and money, and Autodesk’s Revit is the industry leader in the BIM software space.
Contents
Part 1. Fundamentals
Chapter 1. Understanding the Principles of BIM
Chapter 2. Exploring the UI and Organizing Projects
Chapter 3. The Basics of the Toolbox
Chapter 4. Configuring Templates and Standards
Part 2. Collaboration and Teamwork
Chapter 5. Collaborating with a Team
Chapter 6. Working with Consultants
Chapter 7. Interoperability: Working Multiplatform
Chapter 8. Managing Revit Projects
Part 3. Modeling and Massing for Design
Chapter 9. Advanced Modeling and Massing
Chapter 10. Conceptual Design
Chapter 11. Working with Phasing, Groups, and Design Options
Chapter 12. Visualization
Part 4. Extended Modeling Techniques
Chapter 13. Creating Walls and Curtain Walls
Chapter 14. Modeling Floors, Ceilings, and Roofs
Chapter 15. Designing with the Family Editor
Chapter 16. Creating Stairs and Railings
Part 5. Documentation
Chapter 17. Detailing Your Design
Chapter 18. Documenting Your Design
Chapter 19. Annotating Your Design
Part 6. Construction and Beyond
Chapter 20. Working in the Construction Phase
Chapter 21. Presenting Your Design
Chapter 22. Design Analysis
Part 7. Appendixes
Appendix A. The Bottom Line.
Appendix B. Tips, Tricks, and Troubleshooting
Appendix C. Autodesk Revit Certification


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Multilayer Networks Structure and Function

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Multilayer Networks Structure and Function
Multilayer Networks: Structure and Function
By 作者: Ginestra Bianconi
ISBN-10 书号: 0198753918
ISBN-13 书号: 9780198753919
Release Finelybook 出版日期: 2018-08-21
pages 页数: (416 )

$75

Book Description to Finelybook sorting

Multilayer networks is a rising topic in Network Science which characterizes the structure and the function of complex systems formed by several interacting networks. Multilayer networks research has been propelled forward by the wide realm of applications in social, biological and infrastructure networks and the large availability of network data, as well as by the significance of recent results, which have produced important advances in this rapidly growing field. This book presents a comprehensive account of this emerging field. It provides a theoretical introduction to the main results of multilayer network science.

Part I Single and Multilayer Networks
P1Complex Systems as Multilayer Networks
Part ll Single Networks
P2The Structure of Single Networks
3 The Dynamics on Single Networks
4Multilayer Networks in Nature,Society and Infrastructures
5 The Mathematical Definition
6Basic Structural Properties
7 Structural Correlations of Multiplex Networks
8Communities
9 Centrality Measures
10 Multilayer Network Models
11 Interdependent Multilayer Networks
12 Classical Percolation,Generalized Percolation and Cascades
13Epidemic Spreading
14Diffusion
15Synchronization,Non-linear Dynamics and Control
16Opinion Dynamics and Game Theory


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Artificial Intelligence for the Internet of Everything

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Artificial Intelligence for the Internet of Everything
Artificial Intelligence for the Internet of Everything
By 作者: William Lawless
ISBN-10 书号: 0128176369
ISBN-13 书号: 9780128176368
Edition 版本: 1
Release Finelybook 出版日期: 2019-03-11
pages 页数: (303 )

$125

Book Description to Finelybook sorting

Artificial Intelligence for the Internet of Everything considers the foundations, metrics and applications of IoE systems. It covers whether devices and IoE systems should speak only to each other, to humans or to both. Further, the book explores how IoE systems affect targeted audiences (researchers, machines, robots, users) and society, as well as future ecosystems. It examines the meaning, value and effect that IoT has had and may have on ordinary life, in business, on the battlefield, and with the rise of intelligent and autonomous systems. Based on an artificial intelligence (AI) perspective, this book addresses how IoE affects sensing, perception, cognition and behavior.
Each chapter addresses practical, measurement, theoretical and research questions about how these “things” may affect individuals, teams, society or each other. Of particular focus is what may happen when these “things” begin to reason, communicate and act autonomously on their own, whether independently or interdependently with other “things”.

Considers the foundations, metrics and applications of IoE systems
Debates whether IoE systems should speak to humans and each other
Explores how IoE systems affect targeted audiences and society
Discusses theoretical IoT ecosystem models
Contributors
Chapter 1:Introduction
Chapter 2:Uncertainty Quantification in Internet of Battlefield Things
Chapter 3:Intelligent Autonomous Things on the Battlefield
Chapter 4:Active Inference in Multiagent Systems:Context-Driven Collaboration and Decentralized Purpose-Driven Team Adaptation
Chapter 5:Policy lssues Regarding Implementations of Cyber AttackResilience Solutions for Cyber Physical Systems
Chapter 6:Trust and Human-Machine Teaming:A Qualitative Study
Chapter 7:The Web of Smart Entities-Aspects of a Theory of the Next Generation of the Internet of Things
Chapter 8:Raising Them Right:Al and the Internet of Big Things
Chapter 9:The Value of Information and the Internet of Things
Chapter 10:Would IOET Make Economics More Neoclassical or More Behavioral? Richard Thaler’s Prediction,a Revisit
Chapter 11:Accessing Validity of Argumentation of Agents of the Internet of Everything
Chapter 12:Distributed Autonomous Energy Organizations:Next-Generation Blockchain Applications for Energy Infrastructure
Chapter 13:Compositional Models for Complex Systems
Chapter 14:Meta-Agents:Using Multi-Agent Networks to Manage Dynamic Changes in the Internet of Things
Index

Artificial Intelligence for HR: Use AI to Support and Develop a Successful Workforce

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Artificial Intelligence for HR: Use AI to Support and Develop a Successful Workforce
Artificial Intelligence for HR: Use AI to Support and Develop a Successful Workforce
By 作者: Ben Eubanks
ISBN-10 书号: 0749483814
ISBN-13 书号: 9780749483814
Edition 版本: 1
Release Finelybook 出版日期: 2018-12-28
pages 页数: (240 )

$39.95

Book Description to Finelybook sorting

HR professionals need to get to grips with artificial intelligence and the way it’s changing the world of work. From using natural language processing to ensure job adverts are free from bias and gendered language to implementing chatbots to enhance the employee experience, AI has created a variety of opportunities for the HR function. Artificial Intelligence for HR empowers HR professionals to leverage this potential and use AI to improve efficiency and develop a talented and productive workforce. Outlining the current technology landscape as well as the latest AI developments, this book ensures that HR professionals fully understand what AI is and what it means for HR in practice.
Covering everything from recruitment and retention to employee engagement and learning and development, Artificial Intelligence for HR outlines the value AI can add to HR. It also features discussions on the challenges that can arise from AI and how to deal with them, including data privacy, algorithmic bias and how to develop the skills of a workforce with the rise of automation, robotics and machine learning in order to make it more human, not less. Packed with practical advice, research and case studies from global organizations including Uber, IBM and Unilever, this book will equip HR professionals with the knowledge they need to leverage AI to recruit and develop a successful workforce and help their businesses thrive in the future.

Contents


About the author
Foreword by Trish McFarlane
Preface
Acknowledgements
01 A snapshot of HR today
02 The basics of artificial intelligence
03 General Al applications within HCM
04 Core HR and workforce management
05 Talent acquisition
06 Learning and development
07 Talent management
08 Challenges of adopting Al technology
09 HR skills of the future
Afterword
Index
Copyright


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Artificial Intelligence for HR 9780749483814.zip