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Building Android Apps in Python Using Kivy with Android Studio
Start building Python-based Android applications using Kivy with Android Studio. Through in-depth examples, this book teaches you everything you need to create your first Android application in Python and publish on Google Play. Building Android Apps in Python Using Kivy with Android Studio takes you through the basics of Kivy by discussing its application structure, widgets, and event handling. The KV language is then introduced for separating the logic and GUI by adding widgets within a KV file. You will then learn how to utilize Android camera using Kivy, build the HTTP server using Flask, and create and manage multiple screens to help you design your own applications. Through detailed st...
Practical Computer Vision Applications Using Deep Learning with CNNs
Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms. For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate ...
Introduction to Deep Learning and Neural Networks with PythonTM
Introduction to Deep Learning and Neural Networks with PythonTM: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and PythonTM code examples to clarify neural network calculations, by book’s end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and PythonTM examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network. Examines the practical side of deep learning and neural networks Provides a problem-based approach to building artificial neural networks using real data Describes PythonTM functions and features for neuroscientists Uses a careful tutorial approach to describe implementation of neural networks in PythonTM Features math and code examples (via companion website) with helpful instructions for easy implementation
Introduction to Deep Learning and Neural Networks with PythonT
Introduction to Deep Learning and Neural Networks with PythonT: A Practical Guide is an intensive step-by-step guide for neuroscientists to fully understand, practice, and build neural networks. Providing math and PythonT code examples to clarify neural network calculations, by book's end readers will fully understand how neural networks work starting from the simplest model Y=X and building from scratch. Details and explanations are provided on how a generic gradient descent algorithm works based on mathematical and PythonT examples, teaching you how to use the gradient descent algorithm to manually perform all calculations in both the forward and backward passes of training a neural network. Examines the practical side of deep learning and neural networks Provides a problem-based approach to building artificial neural networks using real data Describes PythonT functions and features for neuroscientists Uses a careful tutorial approach to describe implementation of neural networks in PythonT Features math and code examples (via companion website) with helpful instructions for easy implementation
Getting Started with CouchDB
CouchDB is a new breed of database for the Internet, geared to meet the needs of today's dynamic web applications. With this concise introduction, you'll learn how CouchDB's simple model for storing, processing, and accessing data makes it ideal for the type of data and rapid response users now demand from your applications--and how easy CouchDB is to set up, deploy, maintain, and scale. The code-packed examples in this book will help you learn how to work with documents, populate a simple database, replicate data from one database to another, and a host of other tasks. Install CouchDB on Linux, Mac OS X, Windows, or (if you must) from the source code Interact with data through CouchDB's RESTful API, and use standard HTTP operations, such as PUT, GET, POST, and DELETE Use Futon--CouchDB's web-based interface-- to manage databases and documents, and to configure replications Learn how to create, update, and delete documents in JSON format, and how to create and delete databases Work with design documents to get the formatting and indexing your application requires
Creating Apps in Kivy
Build mobile apps efficiently with Kivy, the Python-powered graphical toolkit for creating natural user interfaces with elegant multitouch support. With this hands-on guide, you’ll learn step-by-step how to build and deploy a complete Kivy app for iOS and Android devices. If you’re just beginning to work with Python, but are reasonably familiar with its syntax, you’re ready to go. Each chapter includes exercises, using examples that run on Python 3 and Python 2.7. Learn how Kivy simplifies mobile development with its cross-platform API and domain-specific Kv language, and why this free and open source toolkit is ideal for commercial products. Design custom widgets with the Kv language Delve into Kivy events, event handlers, and properties Dynamically change which Kivy widgets are displayed Understand and apply iterative development principles Create basic animations, using Canvas and graphics primitives Store local data with Kivy’s powerful key value store Add basic gestures to switch between app views Improve your app’s usability with Kivy’s built-in widgets Deploy the app to your Android or iOS device, using Buildozer
IronPython in Action
In 2005, Microsoft quietly announced an initiative to bring dynamic languages to the .NET platform. The starting point for this project was a .NET implementation of Python, dubbed IronPython. After a couple years of incubation, IronPython is ready for real-world use. It blends the simplicity, elegance, and dynamism of Python with the power of the .NET framework. IronPython in Action offers a comprehensive, hands-on introduction to Microsoft's exciting new approach for programming the .NET framework. It approaches IronPython as a first class .NET language, fully integrated with the .NET environment, Visual Studio, and even the open-source Mono implementation. You'll learn how IronPython can b...
Pro Android Python with SL4A
Pro Android Python with SL4A is for programmers and hobbyists who want to write apps for Android devices without having to learn Java first. Paul Ferrill leads you from installing the Scripting Layer for Android (SL4A) to writing small scripts, to more complicated and interesting projects, and finally to uploading and packaging your programs to an Android device. Android runs scripts in many scripting languages, but Python, Lua, and Beanshell are particularly popular. Most programmers know more than one programming language, so that they have the best tool for whatever task they want to accomplish. Pro Android Python with SL4A explores the world of Android scripting by introducing you to the...
Kivy – Interactive Applications and Games in Python
Kivy – Interactive Applications and Games in Python Second Edition, will equip you with all the necessary knowledge to create interactive, responsive, and cross-platform applications and games. This book introduces the Kivy language and the necessary components so you can implement a graphical user interface (GUI) and learn techniques to handle events, detect gestures, and control multi-touch actions. You will learn strategies to animate your applications, and obtain interactive, professional-looking, and responsive results. You will be applying this knowledge throughout the book by developing three applications and tackling their diverse programming challenges.
Numerical Python
Leverage the numerical and mathematical modules in Python and its standard library as well as popular open source numerical Python packages like NumPy, SciPy, FiPy, matplotlib and more. This fully revised edition, updated with the latest details of each package and changes to Jupyter projects, demonstrates how to numerically compute solutions and mathematically model applications in big data, cloud computing, financial engineering, business management and more. Numerical Python, Second Edition, presents many brand-new case study examples of applications in data science and statistics using Python, along with extensions to many previous examples. Each of these demonstrates the power of Python...
