Informatyka
Benjamin Baka
Data structures allow you to organize data in a particular way efficiently. They are critical to any problem, provide a complete solution, and act like reusable code. In this book, you will learn the essential Python data structures and the most common algorithms. With this easy-to-read book, you will be able to understand the power of linked lists, double linked lists, and circular linked lists. You will be able to create complex data structures such as graphs, stacks and queues. We will explore the application of binary searches and binary search trees. You will learn the common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. We will also discuss how to organize your code in a manageable, consistent, and extendable way. The book will explore in detail sorting algorithms such as bubble sort, selection sort, insertion sort, and merge sort. By the end of the book, you will learn how to build components that are easy to understand, debug, and use in different applications.
Indra den Bakker
Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter...
With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects.This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota.By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.
Valentino Zocca, Gianmario Spacagna, Daniel Slater, Peter...
With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries.The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results.Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques.Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you’ll find everything inside.
Matthew Lamons, Rahul Kumar, Abhishek Nagaraja
Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier.Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system.Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects.By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way
Ivan Vasilev, Valentino Zocca
The field of deep learning has developed rapidly recently and today covers a broad range of applications. This makes it challenging to navigate and hard to understand without solid foundations. This book will guide you from the basics of neural networks to the state-of-the-art large language models in use today.The first part of the book introduces the main machine learning concepts and paradigms. It covers the mathematical foundations, the structure, and the training algorithms of neural networks and dives into the essence of deep learning.The second part of the book introduces convolutional networks for computer vision. We’ll learn how to solve image classification, object detection, instance segmentation, and image generation tasks.The third part focuses on the attention mechanism and transformers – the core network architecture of large language models. We’ll discuss new types of advanced tasks they can solve, such as chatbots and text-to-image generation.By the end of this book, you’ll have a thorough understanding of the inner workings of deep neural networks. You'll have the ability to develop new models and adapt existing ones to solve your tasks. You’ll also have sufficient understanding to continue your research and stay up to date with the latest advancements in the field.
Python dla DevOps. Naucz się bezlitośnie skutecznej automatyzacji
Noah Gift, Kennedy Behrman, Alfredo Deza, Grig...
Ostatnia dekada zmieniła oblicze IT. Kluczowego znaczenia nabrały big data, a chmura i automatyzacja rozpowszechniły się wszędzie tam, gdzie mowa o efektywności. Inżynierowie muszą wykorzystywać zalety systemów linuksowych w codziennej praktyce, aby zapewnić należyty poziom automatyzacji swoich zadań. Do tych celów świetnie nadaje się Python. Język ten zdobywa coraz większe uznanie z uwagi na jego wszechstronność, jak również wydajność, przenaszalność i bezpieczeństwo kodu. Warto więc wykorzystywać Pythona do administrowania systemami Linux wraz z takimi narzędziami DevOps jak Docker, Kubernetes i Terraform. Dzięki tej książce dowiesz się, jak sobie z tym poradzić. Znalazło się w niej krótkie wprowadzenie do Pythona oraz do automatyzacji przetwarzania tekstu i obsługi systemu plików, a także do pisania własnych narzędzi wiersza poleceń. Zaprezentowano również przydatne narzędzia linuksowe, systemy zarządzania pakietami oraz systemy budowania, monitorowania i automatycznego testowania kodu. Zagadnienia te szczególnie zainteresują specjalistów DevOps. Ponadto zawarto tu podstawowe informacje o chmurze obliczeniowej, usługach IaC i systemach Kubernetes. Omówiono zasady uczenia maszynowego i inżynierii danych z perspektywy DevOps. Przedstawiono także kompletny przewodnik po procesach budowania, wdrażania oraz operacyjnego wykorzystywania modelu uczenia maszynowego z użyciem systemów Flask, sklearn, Docker i Kubernetes. W tej książce: wprowadzenie do Pythona automatyczne przetwarzanie tekstu oraz automatyzacja operacji na plikach automatyzacja za pomocą sprawdzonych narzędzi linuksowych chmura, infrastruktura jako kod, Kubernetes i tryb bezserwerowy uczenie maszynowe i inżynieria danych z perspektywy DevOps tworzenie i operacjonalizacja projektu uczenia maszynowego Python: tutaj ważna jest prawdziwa nowoczesność oprogramowania!
Python dla nastolatków. Projekty graficzne z Python Turtle
Krzysztof Łos
Książka "Python dla nastolatków. Projekty graficzne z Python Turtle" Krzysztofa Łosa zdobyła wyróżnienie w kategorii podręczników w konkursie na Najlepszą Polską Książkę Informatyczną 2023r. organizowanym przez Polskie Towarzystwo Informatyczne. Każdy może zostać programistą! Czy wiesz, czym się zajmuje programista? To ktoś, kto, używając swojego umysłu i odpowiedniego języka programowania, rozwiązuje rozmaite problemy. Programista to taki współczesny superbohater. Przychodzi, siada do komputera, szybko przebiega palcami po klawiaturze i proszę ― działa. Oczywiście, to pewne uproszczenie, ale... Brzmi ciekawie? Słusznie. Bo praca programisty, kodera, developera jest ciekawa. I fajna. I daje dużo satysfakcji. A najlepsze jest to: podstaw programowania można się szybko nauczyć, po prostu się bawiąc. We własny, ulubiony sposób. Choć Twoim przewodnikiem po świecie programowania w Pythonie będzie żółw, obiecujemy ― praca pójdzie Ci w mig. Na początek nauczysz się konfigurować środowisko pracy, czyli uruchomisz na komputerze wszystko, co przyda się Tobie i żółwiowi. Potem zapoznasz się z językiem Python, z jego zmiennymi, funkcjami i klasami. Następnie zajrzysz do biblioteki turtle i dowiesz się, jak sterować swoim żółwiem. Wreszcie najlepsze: algorytmy. Przekonasz się między innymi, jak za pomocą kodu języka Python i elementów biblioteki turtle wygenerować niesamowite figury geometryczne. UWAGA! Książka jest polecana osobom biorącym udział w konkursie Logia. Informacje o konkursie można znaleźć pod adresem: logia.oeiizk.waw.pl
Python dla programistów. Big Data i AI. Studia przypadków
Paul J. Deitel, Harvey Deitel
Programiści lubią Pythona. Ujmuje ich wyrazistość, zwięzłość i interaktywność kodu, a także bogata kolekcja narzędzi i bibliotek. Zalety te są uzupełniane przez rozwój innych technologii, zwiększającą się dostępność coraz szybszego sprzętu oraz rosnącą przepustowość internetu. Z kolei to wszystko wiąże się z powstawaniem niewyobrażalnych ilości danych, które trzeba magazynować i efektywnie przetwarzać. Większość innowacji w świecie informatyki koncentruje się wokół danych. A z tymi zadaniami można sobie poradzić dzięki imponującym możliwościom Pythona i jego bibliotek. Ta książka ułatwi naukę Pythona metodą analizy i eksperymentów. Zawiera ponad 500 przykładów faktycznie wykorzystywanego kodu - od krótkich bloków po kompletne studia przypadków. Pokazano, w jaki sposób można kodować w interpreterze IPython i notatnikach Jupytera. Znalazł się tu obszerny opis Pythona oraz jego instrukcji sterujących i funkcji, omówiono pracę na plikach, kwestie serializacji w notacji JSON i obsługę wyjątków. Zaprezentowano różne paradygmaty programowania: proceduralnego, w stylu funkcyjnym i zorientowanego obiektowo. Sporo miejsca poświęcono bibliotekom: standardowej bibliotece Pythona i bibliotekom data science do realizacji złożonych zadań przy minimalnym udziale kodowania. Nie zabrakło wprowadzenia do takich zagadnień data science jak sztuczna inteligencja, symulacje, animacje czy przygotowanie danych do analizy. W książce między innymi: przetwarzanie języka naturalnego IBM stosowanie bibliotek scikit-learn i Keras big data, Hadoop(R), Spark™, NoSQL i usługi chmurowe programowanie dla internetu rzeczy (IoT) biblioteki: standardowa, NumPy, Pandas, SciPy, NLTK, YexyBlob, Tweepy, Matplotlib i inne
Python Essentials for AWS Cloud Developers. Run and deploy cloud-based Python applications using AWS
Serkan Sakinmaz
AWS provides a vast variety of services for implementing Python applications, which can pose a challenge for those without an AWS background. This book addresses one of the more predominant problems of choosing the right service and stepping into the implementation of exciting Python apps using AWS.The book begins by showing you how to install Python and create an AWS account, before helping you explore AWS Lambda, EC2, Elastic Beanstalk, and S3 for Python programming. You'll then gain hands-on experience in using these services to build the Python application. As you advance, you'll discover how to debug Python apps using PyCharm, and then start deploying the Python applications on Elastic Beanstalk. You’ll also learn how to monitor Python applications using the CloudWatch service, along with creating and publishing APIs on AWS to access the Python application. The concluding chapters will help you get to grips with storing unstructured and semi-structured data using NoSQL and DynamoDB, as well as advance your knowledge using the Glue serverless data integration service in AWS.By the end of this Python book, you’ll be able to take your application development skills up a notch with AWS services and advance in your career.
Oliver Theobald
This book provides a thorough introduction to Python, starting with basic operations like arithmetic and variable creation. As you progress, you'll delve into more complex topics such as loops, conditionals, functions, and object-oriented programming. By the end, you'll be able to write Python code and use libraries like Pandas to manipulate data efficiently. Practical challenges and exercises help solidify your learning. It’s designed to be engaging and easy to follow, making the Python learning experience as enjoyable as it is informative. As you build your skills, you will also gain hands-on experience by tackling coding exercises that reinforce each concept. Whether you're new to programming or looking to sharpen your Python skills, this book will guide you through every essential aspect of the language, preparing you for real-world applications.
Massimiliano Pippi
If you are a Python developer, whether you have experience in web applications development or not, and want to rapidly deploy a scalable backend service or a modern web application on Google App Engine, then this book is for you.
Mercury Learning and Information, Oswald Campesato
This book is designed for developers with little to no experience in Python or Pandas, providing a fast-paced introduction to Python programming and practical solutions to various tasks. The journey begins with a quick tour of basic Python 3, followed by a deep dive into loops and conditional logic. The text covers data structures extensively, and includes tasks involving strings and arrays.As the reader progresses, object-oriented programming concepts are introduced with illustrative code samples, along with an exploration of recursion and fundamental topics in combinatorics. An appendix provides an introduction to Pandas, equipping readers with essential tools for data manipulation and analysis.This book offers a comprehensive yet concise learning path, reinforced by numerous code samples and companion files available for download. It is an invaluable resource for beginners seeking to master Python and Pandas, providing a solid foundation for further exploration in programming and data science.
José Manuel Ortega
Python’s latest updates add numerous libraries that can be used to perform critical security-related missions, including detecting vulnerabilities in web applications, taking care of attacks, and helping to build secure and robust networks that are resilient to them. This fully updated third edition will show you how to make the most of them and improve your security posture.The first part of this book will walk you through Python scripts and libraries that you’ll use throughout the book. Next, you’ll dive deep into the core networking tasks where you will learn how to check a network’s vulnerability using Python security scripting and understand how to check for vulnerabilities in your network – including tasks related to packet sniffing. You’ll also learn how to achieve endpoint protection by leveraging Python packages along with writing forensics scripts.The next part of the book will show you a variety of modern techniques, libraries, and frameworks from the Python ecosystem that will help you extract data from servers and analyze the security in web applications. You’ll take your first steps in extracting data from a domain using OSINT tools and using Python tools to perform forensics tasks.By the end of this book, you will be able to make the most of Python to test the security of your network and applications.
Ryan Marvin, Mark Nganga, Amos Omondi
After a brief history of Python and key differences between Python 2 and Python 3, you'll understand how Python has been used in applications such as YouTube and Google App Engine. As you work with the language, you'll learn about control statements, delve into controlling program flow and gradually work on more structured programs via functions.As you settle into the Python ecosystem, you'll learn about data structures and study ways to correctly store and represent information. By working through specific examples, you'll learn how Python implements object-oriented programming (OOP) concepts of abstraction, encapsulation of data, inheritance, and polymorphism. You'll be given an overview of how imports, modules, and packages work in Python, how you can handle errors to prevent apps from crashing, as well as file manipulation.By the end of this book, you'll have built up an impressive portfolio of projects and armed yourself with the skills you need to tackle Python projects in the real world.
Erik Westra
Geospatial development links your data to locations on the surface of the Earth. Writing geospatial programs involves tasks such as grouping data by location, storing and analyzing large amounts of spatial information, performing complex geospatial calculations, and drawing colorful interactive maps. In order to do this well, you’ll need appropriate tools and techniques, as well as a thorough understanding of geospatial concepts such as map projections, datums, and coordinate systems.This book provides an overview of the major geospatial concepts, data sources, and toolkits. It starts by showing you how to store and access spatial data using Python, how to perform a range of spatial calculations, and how to store spatial data in a database. Further on, the book teaches you how to build your own slippy map interface within a web application, and finishes with the detailed construction of a geospatial data editor using the GeoDjango framework. By the end of this book, you will be able to confidently use Python to write your own geospatial applications ranging from quick, one-off utilities to sophisticated web-based applications using maps and other geospatial data.