Python

Time Series Analysis with Spark. A practical guide to processing, modeling, and forecasting time series with Apache Spark

Yoni Ramaswami, Dael Williamson, Jan Govaere

Written by Databricks Senior Solutions Architect Yoni Ramaswami, whose expertise in Data and AI has shaped innovative digital transformations across industries, this comprehensive guide bridges foundational concepts of time series analysis with the Spark framework and Databricks, preparing you to tackle real-world challenges with confidence.From preparing and processing large-scale time series datasets to building reliable models, this book offers practical techniques that scale effortlessly for big data environments. You’ll explore advanced topics such as scaling your analyses, deploying time series models into production, Generative AI, and leveraging Spark's latest features for cutting-edge applications across industries. Packed with hands-on examples and industry-relevant use cases, this guide is perfect for data engineers, ML engineers, data scientists, and analysts looking to enhance their expertise in handling large-scale time series data.By the end of this book, you’ll have mastered the skills to design and deploy robust, scalable time series models tailored to your unique project needs—qualifying you to excel in the rapidly evolving world of big data analytics.*Email sign-up and proof of purchase required

Tkinter GUI Application Development Blueprints. Build nine projects by working with widgets, geometry management, event handling, and more - Second Edition

Bhaskar Chaudhary

Tkinter is the built-in GUI package that comes with standard Python distributions. It is a cross-platform package, which means you build once and deploy everywhere. It is simple to use and intuitive in nature, making it suitable for programmers and non-programmers alike.This book will help you master the art of GUI programming. It delivers the bigger picture of GUI programming by building real-world, productive, and fun applications such as a text editor, drum machine, game of chess, audio player, drawing application, piano tutor, chat application, screen saver, port scanner, and much more. In every project, you will build on the skills acquired in the previous project and gain more expertise. You will learn to write multithreaded programs, network programs, database-driven programs, asyncio based programming and more. You will also get to know the modern best practices involved in writing GUI apps. With its rich source of sample code, you can build upon the knowledge gained with this book and use it in your own projects in the discipline of your choice.

Tkinter GUI Application Development Cookbook. A practical solution to your GUI development problems with Python and Tkinter

Alejandro Rodas de Paz

As one of the more versatile programming languages, Python is well-known for its batteries-included philosophy, which includes a rich set of modules in its standard library; Tkinter is the library included for building desktop applications. Due to this, Tkinter is a common choice for rapid GUI development, and more complex applications can benefit from the full capabilities of this library. This book covers all of your Tkinter and Python GUI development problems and solutions.Tkinter GUI Application Development Cookbook starts with an overview of Tkinter classes and at the same time provides recipes for basic topics, such as layout patterns and event handling. Next, we cover how to develop common GUI patterns, such as entering and saving data, navigating through menus and dialogs, and performing long-running actions in the background.You can then make your apps leverage network resources effectively and perform graphical operations on a canvas and related tasks such as detecting collisions between items. Finally, this book covers using themed widgets, an extension of Tk widgets that have a more native look and feel. Finally, this book covers using the canvas and themed widgets.By the end of the book, you will have an in-depth knowledge of Tkinter classes, and will know how to use them to build efficient and rich GUI applications.

Tkinter GUI Programming by Example. Learn to create modern GUIs using Tkinter by building real-world projects in Python

David Love

Tkinter is a modular, cross-platform application development toolkit for Python. When developing GUI-rich applications, the most important choices are which programming language(s) and which GUI framework to use. Python and Tkinter prove to be a great combination. This book will get you familiar with Tkinter by having you create fun and interactive projects. These projects have varying degrees of complexity. We'll start with a simple project, where you'll learn the fundamentals of GUI programming and the basics of working with a Tkinter application. After getting the basics right, we'll move on to creating a project of slightly increased complexity, such as a highly customizable Python editor. In the next project, we'll crank up the complexity level to create an instant messaging app. Toward the end, we'll discuss various ways of packaging our applications so that they can be shared and installed on other machines without the user having to learn how to install and run Python programs.

Training Systems Using Python Statistical Modeling. Explore popular techniques for modeling your data in Python

Curtis Miller

Python's ease-of-use and multi-purpose nature has made it one of the most popular tools for data scientists and machine learning developers. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book is designed to guide you through using these libraries to implement effective statistical models for predictive analytics.You’ll start by delving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will focus on supervised learning, which will help you explore the principles of machine learning and train different machine learning models from scratch. Next, you will work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. The book will also cover algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. In later chapters, you will learn how neural networks can be trained and deployed for more accurate predictions, and understand which Python libraries can be used to implement them.By the end of this book, you will have the knowledge you need to design, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive analytics.

Transformers for Natural Language Processing. Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more

Denis Rothman

The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers.The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face.The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification.By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets.

Transforming Healthcare with DevOps. A practical DevOps4Care guide to embracing the complexity of digital transformation

Jeroen Mulder, Henry Mulder

Healthcare today faces a multitude of challenges, which can be summed up as the barriers architects and consultants face in transforming the healthcare system into a more sustainable one. This book helps you to guide that transformation step by step.You’ll begin by understanding the need for this transformation, exploring related challenges, the possibilities of technology, and how human factors can be involved in digital transformation. The book will enable you to overcome inhibitions and plan various transformation steps using the Transformation into Sustainable Healthcare (TiSH) model and DevOps4Care. Next, you’ll use the observe, orient, decide, and act (OODA) loop as an iterative approach to address all stakeholders and adapt swiftly when situations change. Further, you’ll be able to build shared platforms that enable interaction between various stakeholders, including the technology-enabled care service teams. The final chapters will help you execute the transformation to sustainable healthcare using the knowledge you’ve gained while getting familiar with common pitfalls and learning how to avoid or mitigate them.By the end of this DevOps book, you will have an overview of the challenges, opportunities, and directions of solutions and be on your way toward starting the transformation into sustainable healthcare.

Uczenie głębokie od zera. Podstawy implementacji w Pythonie

Seth Weidman

Uczenie głębokie (ang. deep learning) zyskuje ostatnio ogromną popularność. Jest to ściśle związane z coraz częstszym zastosowaniem sieci neuronowych w przeróżnych branżach i dziedzinach. W konsekwencji inżynierowie oprogramowania, specjaliści do spraw przetwarzania danych czy osoby w praktyce zajmujące się uczeniem maszynowym muszą zdobyć solidną wiedzę o tych zagadnieniach. Przede wszystkim trzeba dogłębnie zrozumieć podstawy uczenia głębokiego. Dopiero po uzyskaniu biegłości w posługiwaniu się poszczególnymi koncepcjami i modelami możliwe jest wykorzystanie w pełni potencjału tej dynamicznie rozwijającej się technologii. Ten praktyczny podręcznik, poświęcony podstawom uczenia głębokiego, zrozumiale i wyczerpująco przedstawia zasady działania sieci neuronowych z trzech różnych poziomów: matematycznego, obliczeniowego i konceptualnego. Takie podejście wynika z faktu, że dogłębne zrozumienie sieci neuronowych wymaga nie jednego, ale kilku modeli umysłowych, z których każdy objaśnia inny aspekt działania tych sieci. Zaprezentowano tu również techniki implementacji poszczególnych elementów w języku Python, co pozwala utworzyć działające sieci neuronowe. Dzięki tej książce stanie się jasne, w jaki sposób należy tworzyć, uczyć i stosować wielowarstwowe, konwolucyjne i rekurencyjne sieci neuronowe w różnych praktycznych zastosowaniach. W książce między innymi: matematyczne podstawy uczenia głębokiego tworzenie modeli do rozwiązywania praktycznych problemów standardowe i niestandardowe techniki treningu sieci neuronowych rozpoznawanie obrazów za pomocą konwolucyjnych sieci neuronowych rekurencyjne sieci neuronowe, ich działanie i implementacja praca z wykorzystaniem biblioteki PyTorch Uczenie głębokie: zrozum, zanim zaimplementujesz!