Название: Learning eBPF: Programming the Linux Kernel for Enhanced Observability, Networking, and Security (3rd Early Release)
Автор: Liz Rice
Издательство: O’Reilly Media, Inc.
Год: 2023-02-13
Страниц: 232
Язык: английский
Формат: pdf, epub, mobi
Размер: 11.5 MB

What is eBPF? With this revolutionary technology, you can write custom code that dynamically changes the way the kernel behaves. It's an extraordinary platform for building a whole new generation of security, observability, and networking tools. This practical book is ideal for developers, system administrators, operators, and students who are curious about eBPF and want to know how it works. Author Liz Rice, chief open source officer with cloud native networking and security specialists Isovalent, also provides a foundation for those who want to explore writing eBPF programs themselves. The entire eBPF program is defined as a string called “program” in the Python code. This C program needs to be compiled before it can be executed, but BCC takes care of that for you. The eBPF program is loaded into the kernel and attached to an event, so the program will be triggered whenever a new executable gets launched on the machine. All that remains to do in the Python code is to read the tracing that is output by the kernel, and write it on screen. eBPF programs can be used to dynamically change the behavior of the system. There’s no need to reboot the machine or restart existing processes - eBPF code starts taking effect as soon as it is attached to an event.

Название: Python for beginners: Your comprehensive step-by-step guide to learn everything about Python
Автор: Daniel Harder
Издательство: TECHHUP
Год: 2022
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

Python was developed by Guido van Rossum in the late eighties and early nineties at the National Research Institute for Mathematics and Computer Science in the Netherlands. Python is derived from many other languages, including ABC, Modula-3, C, C++, Algol-68, SmallTalk, and Unix shell and other scripting languages. Python is copyrighted. Like Perl, Python source code is now available under the GNU General Public License (GPL). Python is now maintained by a core development team at the institute, although Guido van Rossum still holds a vital role in directing its progress.

Название: Effective Machine Learning Teams: Best Practices for ML Practitioners (Early Release)
Автор: David Tan, Ada Leung
Издательство: O’Reilly Media, Inc.
Год: 2023-02-13
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

Gain the valuable skills and techniques you need to accelerate the delivery of Machine Learning solutions. With this practical guide, data scientists and ML engineers will learn how to bridge the gap between data science and Lean software delivery in a practical and simple way. David Tan and Ada Leung from Thoughtworks show you how to apply time-tested software engineering skills and Lean delivery practices that will improve your effectiveness in ML projects. Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help teams avoid common traps in the ML world, so you can iterate more quickly and reliably. With these techniques, data scientists and ML engineers can overcome friction and experience flow when delivering Machine Learning solutions.

Название: C++ for beginners: Your comprehensive step-by-step guide to learn everything about C++
Автор: Daniel Harder
Издательство: TECHHUP
Год: 2022
Страниц: 176
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

C++ (pronounced "C plus plus") is a high-level general-purpose programming language created by Danish computer scientist Bjarne Stroustrup as an extension of the C programming language, or "C with Classes". The language has expanded significantly over time, and modern C++ now has object-oriented, generic, and functional features in addition to facilities for low-level memory manipulation. It is almost always implemented as a compiled language, and many vendors provide C++ compilers, including the Free Software Foundation, LLVM, Microsoft, Intel, Embarcadero, Oracle, and IBM, so it is available on many platforms. C++ was designed with systems programming and embedded, resource-constrained software and large systems in mind, with performance, efficiency, and flexibility of use as its design highlights.C++ has also been found useful in many other contexts, with key strengths being software infrastructure and resource-constrained applications, including desktop applications, video games, servers (e.g. e-commerce, web search, or databases), and performance-critical applications (e.g. telephone switches or space probes).

Название: Python Data Science Handbook: Essential Tools for Working with Data, 2nd Edition (Final Release)
Автор: Jake VanderPlas
Издательство: O’Reilly Media, Inc.
Год: 2023
Страниц: 591
Язык: английский
Формат: True/Retail PDF EPUB
Размер: 30.8 MB

Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, pandas, Matplotlib, scikit-learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

Название: Practicing Trustworthy Machine Learning: Consistent, Transparent, and Fair AI Pipelines (Final Release)
Автор: Yada Pruksachatkun, Matthew McAteer, Subhabrata Majumdar
Издательство: O’Reilly Media, Inc.
Год: 2023
Страниц: 303
Язык: английский
Формат: True/Retail PDF EPUB
Размер: 47.0 MB

With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. This book is written for anyone who is currently working with Machine Learning models and wants to be sure that the fruits of their labor will not cause unintended harm when released into the real world. The primary audience of the book are engineers and data scientists who have some familiarity with Machine Learning. Parts of the book should be accessible to non-engineers, such as product managers and executives with a conceptual understanding of ML. Some of you may be building ML systems that make higher-stakes decisions than they encountered in your previous job or in academia. We assume you are are familiar with the very basics of Deep Learning, and Python for the code samples.

Название: A Student's Guide to Python for Physical Modeling, 2nd Edition
Автор: Jesse M. Kinder, Philip Nelson
Издательство: Princeton University Press
Год: 2021
Страниц: 241
Язык: английский
Формат: pdf (true)
Размер: 10.3 MB

A fully updated tutorial on the basics of the Python programming language for science students. Python is a computer programming language that has gained popularity throughout the sciences. This fully updated second edition of A Student's Guide to Python for Physical Modeling aims to help you, the student, teach yourself enough of the Python programming language to get started with physical modeling. You will learn how to install an open-source Python programming environment and use it to accomplish many common scientific computing tasks: importing, exporting, and visualizing data; numerical analysis; and simulation. No prior programming experience is assumed. Numerous code samples and exercises—with solutions—illustrate new ideas as they are introduced. This guide also includes supplemental online resources: code samples, data sets, tutorials, and more. This edition includes new material on symbolic calculations with SymPy, an introduction to Python libraries for Data Science and Machine Learning (Pandas and Sklearn), and a primer on Python classes and object-oriented programming. A new appendix also introduces command line tools and version control with Git.

Название: Machine Learning under Resource Constraints, Volume 3: Applications
Автор: Katharina Morik, Jorg Rahnenfuhrer, Christian Wietfeld
Издательство: De Gruyter
Год: 2023
Страниц: 478
Язык: английский
Формат: pdf (true), epub
Размер: 90.8 MB

Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Volume 3 describes how the resource-aware Machine Learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples. In the areas of health and medicine, it is demonstrated how Machine Learning can improve risk modelling, diagnosis, and treatment selection for diseases. Machine Learning supported quality control during the manufacturing process in a factory allows to reduce material and energy cost and save testing times is shown by the diverse real-time applications in electronics and steel production as well as milling. Additional application examples show, how machine-learning can make traffic, logistics and smart cities more effi cient and sustainable. Finally, mobile communications can benefi t substantially from Machine Learning, for example by uncovering hidden characteristics of the wireless channel.

Название: Machine Learning under Resource Constraints, Volume 2: Discovery in Physics
Автор: Katharina Morik, Wolfgang Rhode
Издательство: De Gruyter
Год: 2023
Страниц: 364
Язык: английский
Формат: pdf (true), epub
Размер: 56.9 MB

Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Volume 2 covers Machine Learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors or telescopes, gather petabytes of data. Here, Machine Learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by Machine Learning.

Название: Machine Learning under Resource Constraints, Volume 1: Fundamentals
Автор: Katharina Morik, Peter Marwedel
Издательство: De Gruyter
Год: 2023
Страниц: 506
Язык: английский
Формат: pdf (true), epub
Размер: 45.7 MB

Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel Machine Learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to Machine Learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness.