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Introduction to Psychophysics with Python
Introduction to Psychophysics with Python is a hands-on guide to exploring how we sense and perceive the world—powered by modern Python programming. Bridging classical theory with today’s computational tools, it equips students and researchers to design, run, and analyze psychophysical experiments with confidence.Starting from the foundations laid by Weber, Fechner, and Stevens, the book traces the evolution of psychophysics into contemporary practice. Readers gain a clear grounding in sensory physiology, experimental design, and statistical analysis, always tied to the central challenge: connecting physical stimuli to perceptual responses and making sense of the data.What sets this book apart is its practical, step-by-step approach. Through tutorials and annotated code using PsychoPy, readers learn Python essentials—loops, conditionals, data handling—while building real experiments. These skills extend seamlessly into advanced methods like curve fitting, bootstrapping, and statistical inference.Illustrated with intuitive explanations, diagrams, and thought-provoking exercises, the book blends conceptual clarity with real-world application. By the end, readers will not only understand the principles of psychophysics but also have the coding expertise to create and analyze their own experiments—opening the door to deeper insights into sensation and perception.
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Python for Scientists
The third edition of this practical introduction to Python has been thoroughly updated, with all code migrated to Jupyter notebooks. The notebooks are available online with executable versions of all of the book's content (and more). The text starts with a detailed introduction to the basics of the Python language, without assuming any prior knowledge. Building upon each other, the most important Python packages for numerical math (NumPy), symbolic math (SymPy), and plotting (Matplotlib) are introduced, with brand new chapters covering numerical methods (SciPy) and data handling (Pandas). Further new material includes guidelines for writing efficient Python code and publishing code for other users. Simple and concise code examples, revised for compatibility with Python 3, guide the reader and support the learning process throughout the book. Readers from all of the quantitative sciences, whatever their background, will be able to quickly acquire the skills needed for using Python effectively.
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Defense in Depth
Up-to-date reference on the evolving landscape of cybersecurity, focusing on strategies that integrate AI, blockchain, and human factors Defense in Depth delivers a comprehensive guide to the rapidly developing field of digital threats and defenses. This book examines how defense-in-depth approaches integrate multiple layers of security to protect against a wide range of threats; how AI technologies are being utilized for threat detection, response, and prediction; and how blockchain technology can be leveraged to enhance security measures, particularly in ensuring data integrity and securing transactions. Case studies of significant cyberattacks are included throughout the text to elucidate key concepts. The book discusses additional topics such as: The critical importance of cybersecurity in today’s interconnected world and the historical evolution of cyber threats, highlighting modern attack vectorsCore principles of defense, detailing layered security measures across networks, applications, data, and endpointsHuman factors, often the weakest link in cybersecurity, with strategies for mitigating risksMachine learning and neural networks and their usage in AI-driven security solutions across finance, healthcare, and governmentAdvanced defense techniques, practical applications, and future directions in cybersecurity Defense in Depth is an essential reference on the state of the art in the field for cybersecurity professionals, IT practitioners, and researchers. It is also an excellent learning aid for advanced students in cybersecurity, particularly those in graduate programs.
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Cloud Security
The book reviews the zero-trust model that deviates from the traditional definition of trust boundaries and validates all interactions in a cloud environment. The incident response life cycle is used to prepare for, identify, contain, and eradicate security threats in the cloud. It discusses how automation and autonomous cyber defense tools based on Artificial Intelligence and Machine Learning and techniques can help resourceconstrained security teams address cloud security at a scale. The book also elaborates upon some helpful case studies on the practical deployment of cloud security solutions, their limitations, and lessons learned based on case-studies experience in cloud security.
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Python 3 - Intensivkurs
Python ist eine vollwertige Programmiersprache, mit der sich auch größere Anwendungen entwickeln lassen. In den letzten Jahren hat sie an Beliebtheit gewonnen, und mit Python 3 steht eine stark erweiterte Version zur Verfügung. In dem Band werden die Werkzeuge und Programmiermöglichkeiten praxisorientiert vorgestellt. Jedes Kapitel beginnt mit einem vollwertigen, lauffähigen Codebeispiel, das jeweils ausführlich analysiert wird. Im Vordergrund stehen die unmittelbare praktische Anwendung und die Realisierung von Projekten mit Python 3.
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Python Programming for Data Analysis
This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns.After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly.The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.
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Python Experiments in Physics and Astronomy
Python Experiments in Physics and Astronomy acts as a resource for science and engineering students or faculty who would like to see how a diverse selection of topics can be analyzed and simulated using Python programs. The book also provides Python solutions that can be learned from and modified as needed. The book is mainly aimed at undergraduates, but since many science students and faculty have limited exposure to scientific programming, having a collection of examples that address curve-fitting, Fast Fourier Transforms, image photometry and image alignment, and many others could be very helpful not just for learning from, but also to support classroom projects and demonstrations.Key Features:Features tutorials using Python for non-computer science students and faculty involved with scientific programmingContains complete scientific programming examples for teaching and academic projectsPresents detailed Python solutions for Physics and Astronomy topics, not normally covered in depth, because they would be too time-consumingPurchasers of the book can access the accompanying computer code via the Instructor Resources Download Hub.
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Coding Club Python: Building Big Apps Level 3
A unique series that provides a framework for teaching coding skills. Build more sophisticated apps with level 3 of Coding Club! Following on from Python: Next Steps, this lively book explains how to break down projects into manageable chunks, guiding and supporting you with building larger, more exciting projects. Activities include building a version of Pong and, using the classes created for Pong, building a Breakout game and an Invaders game, developing and learning the necessary skills to build larger apps on the way. The code is suitable for Mac, Windows and Linux users and therefore compatible with the Raspberry Pi.
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Python for Scientific Computing and Artificial Intelligence
Python for Scientific Computing and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI).This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling.Features:No prior experience of programming is requiredOnline GitHub repository available with codes for readers to practiceCovers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computingFull solutions to exercises are available as Jupyter notebooks on the WebSupport MaterialGitHub Repository of Python Files and Notebooks: https://github.com/proflynch/CRC-Press/Solutions to All Exercises:Section 1: An Introduction to Python: https://drstephenlynch.github.io/webpages/SolutionsSection1.htmlSection 2: Python for Scientific Computing: https://drstephenlynch.github.io/webpages/SolutionsSection2.htmlSection 3: Artificial Intelligence: https://drstephenlynch.github.io/webpages/SolutionsSection3.html
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Python Crash Course, 3rd Edition
Since its initial debut in 2015, this critically acclaimed quick-start guide to programming has taught millions of people all over the world to write clean code, solve problems, and build custom applications in the popular language of Python. The highly anticipated third edition of Python Crash Course has been completely revised with updated code, practices, and projects-making it the ultimate launchpad for beginners to start their engines and code in Python 3!
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