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: Pydon'ts: Write elegant Python code (Update 2023)
: Rodrigo Girao Serrao
: Leanpub
: 26-11-2023
: 462
: pdf (true)
: 13.9 MB

Python has so many libraries that people often forget to take their time to learn about all the really interesting and useful features that Python offers. The Pydon'ts teach you these core features of Python, with plenty of code examples to show you how these features are used in real code in the real world. Python was not my first programming language, and I remember picking it up as a friend of mine recommended it to me. Now, many years later, I still enjoy writing Python code, whether for work-related reasons or for my own projects. In programming, much like in mathematics my main area of expertise , there is a sense of elegance in the code (or proofs) we write. As I learned more and more about programming in general and Python in particular, I developed a sense for what I consider to be elegant Python programs. This is one of the things I intend to share in this book: tips on how to write beautiful Python programs. Of course, the notion of elegance is a subjective one, so it may very well be the case that what I find elegant is not what you find elegant, and that is perfectly fine. In general, neither one of us will be wrong. Tied to my effort of sharing my interpretation of what elegant Python programs look like, I also want you to learn about all the nooks and crannies of the core language. Python is a very, very, rich language, and the more you learn about it, the more well equipped you will be to use it to its full potential. That is why every chapter focuses on exploring a single feature of the core language of Python, which is always accompanied by usage examples of said feature. Some times we will look at how Pythons own Standard Library makes use of that feature, other times I will show some of my own code, and other times I will even come up with random examples.
: Ingvar16 30-11-2023, 21:03 | : 0 |
: Deep Learning for Medical Image Analysis, 2nd Edition
: S. Kevin Zhou, Hayit Greenspan, Dinggang Shen
: Academic Press/Elsevier
: 2024
: 544
: pdf (true)
: 23.5 MB

This book is a detailed reference guide on Deep Learning and its applications. It aims to provide a basic understanding of Deep Learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by Computer Science academics and researchers. By the end of the book, the reader will become familiar with different Deep Learning approaches and models, and understand how to implement various Deep Learning algorithms using multiple frameworks and libraries. This book is divided into three parts. The first part explains the basic operating understanding, history, evolution, and challenges associated with Deep Learning. The basic concepts of mathematics and the hardware requirements for Deep Learning implementation, and some of its popular frameworks for medical applications are also covered. The second part is dedicated to sentiment analysis using Deep Learning and Machine Learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented. The final part covers Artificial Intelligence approaches used to explain the Machine Learning models that enhance transparency for the benefit of users.
: Ingvar16 30-11-2023, 19:38 | : 0 |
: Dirty Data Processing for Machine Learning
: Zhixin Qi, Hongzhi Wang, Zejiao Dong
: Springer
: 2024
: 141
: pdf
: 10.2 MB

In both the database and Machine Learning communities, data quality has become a serious issue which cannot be ignored. In this context, we refer to data with quality problems as dirty data. Clearly, for a given data mining or Machine Learning task, dirty data in both training and test datasets can affect the accuracy of results. Accordingly, this book analyzes the impacts of dirty data and explores effective methods for dirty data processing. Although existing data cleaning methods improve data quality dramatically, the cleaning costs are still high. If we knew how dirty data affected the accuracy of Machine Learning models, we could clean data selectively according to the accuracy requirements instead of cleaning all dirty data, which entails substantial costs. However, no book to date has studied the impacts of dirty data on Machine Learning models in terms of data quality. Filling precisely this gap, the book is intended for a broad audience ranging from researchers in the database and Machine Learning communities to industry practitioners. Readers will find valuable takeaway suggestions on: model selection and data cleaning; incomplete data classification with view-based decision trees; density-based clustering for incomplete data; the feature selection method, which reduces the time costs and guarantees the accuracy of Machine Learning models; and cost-sensitive decision tree induction approaches under different scenarios.
: Ingvar16 30-11-2023, 17:00 | : 0 |
: Software Architecture and Decision-Making: Leveraging Leadership, Technology, and Product Management to Build Great Products (Final)
: Srinath Perera
: Addison-Wesley Professional/Pearson Education
: 2024
: 270
: pdf, epub, mobi
: 10.1 MB

Leverage leadership knowledge to make better software architecture decisions. Think deeply but implement slowly. The overarching goal of software systems (hence, for software architecture) is to build systems that meet quality standards and that provide the highest return on investment (ROI) in the long run or within a defined period of time. A great product requires a combination of technology, leadership, and product management (including UX). Leadership is primarily about managing uncertainty and making the right judgment. To build great products, technical leaders need to combine technology, leadership, and product management knowledge, and make the right decisions. Many technical mistakes come from the gap between knowledge about these three items and judgment. In Software Architecture and Decision-Making, Srinath Perera explains principles and concepts that software architects must understand deeply and how to employ those principles to manage uncertainty. The questions and principles discussed in this book help manage uncertainty while building software architecture and provide a framework for making decisions. This book is for all technical leaders in the software industry who make holistic judgments about the systems they build and for future leaders learning the craft.
: Ingvar16 30-11-2023, 16:15 | : 0 |
: Picture Fuzzy Logic and Its Applications in Decision Making Problems
: Chiranjibe Jana, Madhumangal Pal, Valentina Emilia Balas, Ronald R. Yager
: Academic Press/Elsevier
: 2024
: 296
: pdf (true), epub
: 15.6 MB

Picture Fuzzy Logic and Its Applications in Decision Making Problems provides methodological frameworks and the latest empirical research findings in the field of picture fuzzy operators and their applications in scientific research and real-world engineering problems. In this book, picture fuzzy sets are investigated, and different types of operators are defined to solve a number of important decision-making and optimization problems. The hybrid operator on picture fuzzy set based on the combination of picture fuzzy weighted averaging operators and picture fuzzy weighted geometric operators is developed and named Hybrid Picture Fuzzy Weighted Averaging Geometric (H-PFWAG) operator. In addition, another operator is developed for interval-valued picture fuzzy environment, which is named Hybrid Interval-Valued Picture Fuzzy Weighted Averaging Geometric (H-IVPFWAG) operator. These two operators are then demonstrated as solutions to Multiple-Attribute Decision-Making (MADM) problems. The picture fuzzy soft weighted aggregation operators (averaging and geometric) are defined, and these are applied to develop a multi-criteria group decision making system.
: Ingvar16 30-11-2023, 07:42 | : 0 |

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: polyanskiy 30-11-2023, 07:20 | : 0 |

: Modern TypeScript: A Practical Guide to Accelerate Your Development Velocity
: Ben Beattie-Hood
: Apress
: 2023
ISBN: 978-1484297223
: 308
: 21 M
: English

Dive into the world of TypeScript, a popular superset of the javascript language that enhances the development experience with static typing. This book covers every aspect of TypeScript in a concise and easy-to-understand style, making it the ultimate resource for mastering TypeScript.
: Gena_Bukin 30-11-2023, 00:37 | : 0 |
: iOS 17 App Development for Beginners: Get started with iOS app development using Swift 5.9, SwiftUI, and Xcode 15
: Arpit Kulsreshtha
: BPB Publications
: 2024
: 398
: epub (true)
: 10.1 MB

Learn iOS app development from scratch and build your dream app. iOS 17 App Development for Beginners is a definitive guide to building iOS apps with Swift. This book teaches the fundamentals of Swift, laying the foundation for future app development. It covers how to develop user interfaces for iOS apps using SwiftUI and UIKit and how to write code for views, view controllers, and data managers. The book also teaches using Core Data, Swift Data, and SQLite for database storage. Additionally, it covers essential Apple technologies and frameworks, such as Core Location and MapKit for GPS tracking, Camera and Photo Library for image storage, CI/CD, and Core ML for Machine Learning and Artificial Intelligence solutions. After completing this book, you will have a solid grasp of Swift app development and successfully publish your apps to the App Store. This book is an excellent resource for anyone who wants to learn how to program in Swift and develop applications for the iOS platform. Whether you are a beginner, a student, or a professional, this book will teach you the basics of Swift and how to use it to create your apps. No prior programming experience is necessary, but some familiarity with other programming languages will be helpful.
: Ingvar16 29-11-2023, 20:10 | : 0 |
: Quantum Artificial Intelligence with Qiskit
: Andreas Wichert
: CRC Press
: 2024
: 326
: pdf (true)
: 14.2 MB

Quantum Artificial Intelligence (QAI) is a new interdisciplinary research field that combines quantum computing with Artificial Intelligence (AI), aiming to use the unique properties of quantum computers to enhance the capabilities of AI systems. This book provides a cohesive overview of the field of QAI, providing the tools for readers to create and manipulate quantum programs on devices as accessible as a laptop computer. Introducing symbolical quantum algorithms, sub symbolical quantum algorithms and quantum Machine Learning (ML) algorithms, this book explains each process step-by-step with associated QISKIT listings. All examples are additionally available for download at GitHub. Quantum Artificial Intelligence (QAI) is a new interdisciplinary research field that combines Quantum Computing with Artificial Intelligence (AI). It aims to use the unique properties of quantum computers, which leverage quantum mechanical effects (such as superposition and entanglement) to enhance the capabilities of AI systems. In QAI, progress is being made quickly. Quantum algorithms for AI have been proposed, including a quantum tree search algorithm and a quantum production system that will be demonstrated by Qiskit simulation step by step. Qiskit is an open-source software development kit (SDK) for working with quantum computers at the level of circuits and algorithms, IBM Quantum. It provides tools for creating and manipulating quantum programs and running them on prototype quantum devices on IBM Quantum Experience or on simulators on a local computer. It follows the quantum circuit model for universal quantum computation and can be used for any quantum hardware that follows this model. Qiskit is based on Python and you can find all information about it at qiskit.org. This book provides tools for creating and manipulating quantum programs and running them on prototype quantum devices or simulators on a local computer, such as a simple personal laptop.
: Ingvar16 29-11-2023, 19:09 | : 0 |
: Demystifying Deep Learning: An Introduction to the Mathematics of Neural Networks
: Douglas J. Santry
: Wiley-IEEE Press
: 2024
: 259
: pdf (true)
: 10.1 MB

Discover how to train Deep Learning models by learning how to build real Deep Learning software libraries and verification software! The study of Deep Learning and Artificial Neural Networks (ANN) is a significant subfield of artificial intelligence (AI) that can be found within numerous fields: medicine, law, financial services, and science, for example. AI is everywhereon the news, in think tanks, and occupies government policy makers all over the world and ANNs often provide the backbone for AI. Relying on an informal and succinct approach,Demystifying Deep Learning is a useful tool to learn the necessary steps to implement ANN algorithms by using both a software library applying neural network training and verification software. The volume offers explanations of how real ANNs work, and includes 6 practical examples that demonstrate in real code how to build ANNs and the datasets they need in their implementation, available in open-source to ensure practical usage. This approachable book follows ANN techniques that are used every day as they adapt to Natural Language Processing (NLP), image recognition, problem solving, and generative applications. This volume is an important introduction to the field, equipping the reader for more advanced study. Demystifying Deep Learning is ideal for engineers and professionals that need to learn and understand ANNs in their work.
: Ingvar16 29-11-2023, 18:10 | : 0 |

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