Python GUI Programming Cookbook: Develop functional and responsive user interfaces with tkinter and PyQt5, 3rd Edition Books by Burkhard Meier Over 90 recipes to help you develop widgets, forms, layouts, charts, and much more using the latest features of Python 3 Key Features Use object-oriented programming to develop impressive GUIs in Python Create interesting charts to visually represent data using Matplotlib Develop GUIs with the latest versions of tkinter, PyQt5, and wxPython frameworks Book Description Python is a multi-domain, interpreted programming language that is easy to learn and implement. With its wide support for frameworks to develop GUIs, you can build interactive and beautiful GUI-based applications easily using Python. This third edition of Python GUI Programming Cookbook follows a task-based approach to help you create effective GUIs with the smallest amount of code. Every recipe in this book builds upon the last to create an entire, real-life GUI application. These recipes also help you solve problems that you might encounter while developing GUIs.

JAVA PROGRAMMING, Sixth Edition provides the beginning programmer with a guide to developing applications using the Java programming language. Java is popular among professional programmers because it can be used to build visually interesting GUI and Web-based applications. Java also provides an excellent environment for the beginning programmer – students can quickly build useful programs while learning the basics of structured and object-oriented programming techniques.

With Hands-On Neural Networks with TensorFlow 2.0 - 1st Edition, Understand the basics of machine learning and discover the power of neural networks and deep learning, Explore the structure of the TensorFlow framework and understand how to transition to TF 2.0, Solve any deep learning problem by developing neural network-based solutions using TF 2.0 Description TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers. Book Details Author(s): Paolo Galeone Publisher: Packt Publishing Published: September 18, 2019 Language: English ISBN-10: 1789615550 ISBN-13: 9781789615555 Number of pages: 358 Get This Book Hands-On Neural Networks with TensorFlow 2.0: Understand TensorFlow, from static graph to eager execution, and design neural networks 1st Edition

This text is intended for a 1-, 2-, or 3-semester CS1 course sequence. Comprehensive coverage of Java and programming make this a useful reference for beginning programmers and IT professionals. Daniel Liang teaches concepts of problem-solving and object-oriented programming using a fundamentals-first approach. Beginning programmers learn critical problem-solving techniques then move on to grasp the key concepts of object-oriented, GUI programming, advanced GUI and Web programming using Java. Liang approaches Java GUI programming using JavaFX, not only because JavaFX is much simpler for new Java programmers to learn and use but because it has replaced Swing as the new GUI tool for developing cross-platform-rich Internet applications on desktop computers, on hand-held devices, and on the Web.

Learn everything you need to know about object-oriented JavaScript with this comprehensive guide. Enter the world of cutting-edge development! About This Book This book has been updated to cover all the new object-oriented features introduced in ECMAScript 6 It makes object-oriented programming accessible and understandable to web developers Write better and more maintainable JavaScript code while exploring interactive examples that can be used in your own scripts Who This Book Is For This book is ideal for new to intermediate JavaScript developers who want to prepare themselves for web development problems solved by object-oriented JavaScript! What You Will Learn Apply the basics of object-oriented programming in the JavaScript environment Use a JavaScript Console with complete mastery Make your programs cleaner, faster, and compatible with other programs and libraries Get familiar with Iterators and Generators, the new features added in ES6 Find out about ECMAScript 6†s Arrow functions, and make them your own Understand objects in Google Chrome developer tools and how to use Them Use a mix of prototypal inheritance and copying properties in your workflow Apply reactive programming techniques while coding in JavaScript In Detail JavaScript is an object-oriented programming language that is used for website development. Web pages developed today currently follow a paradigm that has three clearly distinguishable parts: content (HTML), presentation (CSS), and behavior (JavaScript).

Emotions recognition is the task of recognizing people's emotions. Usually it is achieved by analyzing expression of peoples faces. There are two ways for representing emotions: The categorical approach and the dimensional approach by using valence and arousal values. Valence shows how negative or positive an emotion is and arousal shows how much it is activated. Recent deep learning models, that have to do with emotions recognition, are using the second approach, valence and arousal. Moreover, a more interesting concept, which is useful in real life is the "in the wild" emotions recognition. "In the wild" means that the images analyzed for the recognition task, come from from real life sources(online videos, online photos, etc.) and not from staged experiments. So, they introduce unpredictable situations in the images, that have to be modeled. The purpose of this project is to study the previous work that was done for the "in the wild" emotions recognition concept, design a new dataset which has as a standard the "Aff-wild" database, implement new deep learning models and evaluate the results. First, already existing databases and deep learning models are presented. Then, inspired by them a new database is created which includes 507.208 frames in total from 106 videos, which were gathered from online sources. Then, the data are tested in a CNN model based on CNN-M architecture, in order to be sure about their usability. Next, the main model of this project is implemented. That is a Regression GAN which can execute unsupervised and supervised learning at the same time. More specifically, it keeps the main functionality of GANs, which is to produce fake images that look as good as the real ones, while it can also predict valence and arousal values for both real and fake images. Finally, the database created earlier is applied to this model and the results are presented and evaluated.

The journal continues to take on big and tough issues as exemplified by the November 2018 special issue "Machine Learning in Health and Biomedicine." As computational power increases exponentially, the capacity to (more affordably) handle, store, and analyze "big data" using machine learning (ML) will revolutionize science and medicine. The power of ML is to find patterns among variables in large data sets rather than being programmed with rules. Models become more complex when they move from supervised (input and outputs have labels) to unsupervised (no labels), and when they move from linear regression with decision trees to neural networks ( 3 neural networks is termed deep learning). As the complexity increases so does one's ability to "interpret" the data.

Find powerful new insights in your data; discover machine learning with R.

The computer industry faces epic change, as the demands of "deep learning" forms of machine learning force new requirements upon silicon, at the same time that Moore's Law, the decades-old rule of progress in the chip business, is collapsing. This week, some of the best minds in the chip industry gathered in San Francisco to talk about what it means. Applied Materials, the dominant maker of tools to fabricate transistors, sponsored a full day of keynotes and panel sessions on Tuesday, called the "A.I. The presentations and discussions had good news and bad news. On the plus side, many tools are at the disposal of companies such as Advanced Micro Devices and Xilinx to make "heterogenous" arrangements of chips to meet the demands of deep learning. On the downside, it's not entirely clear that what they have in their kit bag will mitigate a potential exhaustion of data centers under the weight of increased computing demand. No new chips were shown at the Semicon show, those kinds of unveilings long since passed to other trade shows and conferences. But the discussion at the A.I. forum gave a good sense of how the chip industry is thinking about the explosion of machine learning and what it means for computers. Gary Dickerson, chief executive of Applied Materials, started his talk by noting the "dramatic slowdown of Moore's Law, citing data from UC Berkeley Professor David Patterson and Alphabet chairman John Hennessy showing that new processors are improving in performance by only 3.5% per year.

Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongly from nonliving. It is, therefore, not surprising that this aspect of life should become a central focus of artificial life. We have known since Darwin that the diversity is produced dynamically, through the process of evolution; this has led life's creative productivity to be called Open-Ended Evolution (OEE) in the field. This article introduces the second of two special issues on current research in OEE and provides an overview of the contents of both special issues. Most of the work was presented at a workshop on open-ended evolution that was held as a part of the 2018 Conference on Artificial Life in Tokyo, and much of it had antecedents in two previous workshops on open-ended evolution at artificial life conferences in Cancun and York. We present a simplified categorization of OEE and summarize progress in the field as represented by the articles in this special issue.