Hands On Neuroevolution With Python
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Hands On Neuroevolution With Python
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Author : Iaroslav Omelianenko
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-12-24
Hands On Neuroevolution With Python written by Iaroslav Omelianenko and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-24 with Computers categories.
Increase the performance of various neural network architectures using NEAT, HyperNEAT, ES-HyperNEAT, Novelty Search, SAFE, and deep neuroevolution Key FeaturesImplement neuroevolution algorithms to improve the performance of neural network architecturesUnderstand evolutionary algorithms and neuroevolution methods with real-world examplesLearn essential neuroevolution concepts and how they are used in domains including games, robotics, and simulationsBook Description Neuroevolution is a form of artificial intelligence learning that uses evolutionary algorithms to simplify the process of solving complex tasks in domains such as games, robotics, and the simulation of natural processes. This book will give you comprehensive insights into essential neuroevolution concepts and equip you with the skills you need to apply neuroevolution-based algorithms to solve practical, real-world problems. You'll start with learning the key neuroevolution concepts and methods by writing code with Python. You'll also get hands-on experience with popular Python libraries and cover examples of classical reinforcement learning, path planning for autonomous agents, and developing agents to autonomously play Atari games. Next, you'll learn to solve common and not-so-common challenges in natural computing using neuroevolution-based algorithms. Later, you'll understand how to apply neuroevolution strategies to existing neural network designs to improve training and inference performance. Finally, you'll gain clear insights into the topology of neural networks and how neuroevolution allows you to develop complex networks, starting with simple ones. By the end of this book, you will not only have explored existing neuroevolution-based algorithms, but also have the skills you need to apply them in your research and work assignments. What you will learnDiscover the most popular neuroevolution algorithms – NEAT, HyperNEAT, and ES-HyperNEATExplore how to implement neuroevolution-based algorithms in PythonGet up to speed with advanced visualization tools to examine evolved neural network graphsUnderstand how to examine the results of experiments and analyze algorithm performanceDelve into neuroevolution techniques to improve the performance of existing methodsApply deep neuroevolution to develop agents for playing Atari gamesWho this book is for This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking to implement neuroevolution algorithms from scratch. Working knowledge of the Python programming language and basic knowledge of deep learning and neural networks are mandatory.
Proceedings Of Tenth International Congress On Information And Communication Technology
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Author : Xin-She Yang
language : en
Publisher: Springer Nature
Release Date : 2025-10-07
Proceedings Of Tenth International Congress On Information And Communication Technology written by Xin-She Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-07 with Technology & Engineering categories.
This book gathers selected high-quality research papers presented at the Tenth International Congress on Information and Communication Technology (ICICT 2025), held in London, on February 18–21, 2025. It discusses emerging topics pertaining to information and communication technology (ICT) for managerial applications, e-governance, e-agriculture, e-education and computing technologies, the Internet of Things (IoT), and e-mining. Written by respected experts and researchers working on ICT, the book offers an asset for young researchers involved in advanced studies. The work is presented in ten volumes.
Ui Testing With Puppeteer
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Author : Dario Kondratiuk
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-03-11
Ui Testing With Puppeteer written by Dario Kondratiuk and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-11 with Computers categories.
Write fail-safe automation tests, device emulation, and browser automation using Puppeteer’s Google-powered API Key FeaturesGet up and running with Puppeteer and discover best practices for automation testingAutomate your modern web applications using Jest and Mocha with PuppeteerGenerate screenshots using Puppeteer and find out how they can be used for UI regression testsBook Description Puppeteer is an open source web automation library created by Google to perform tasks such as end-to-end testing, performance monitoring, and task automation with ease. Using real-world use cases, this book will take you on a pragmatic journey, helping you to learn Puppeteer and implement best practices to take your automation code to the next level! Starting with an introduction to headless browsers, this book will take you through the foundations of browser automation, showing you how far you can get using Puppeteer to automate Google Chrome and Mozilla Firefox. You’ll then learn the basics of end-to-end testing and understand how to create reliable tests. You’ll also get to grips with finding elements using CSS selectors and XPath expressions. As you progress through the chapters, the focus shifts to more advanced browser automation topics such as executing JavaScript code inside the browser. You’ll learn various use cases of Puppeteer, such as mobile devices or network speed testing, gauging your site’s performance, and using Puppeteer as a web scraping tool. By the end of this UI testing book, you’ll have learned how to make the most of Puppeteer’s API and be able to apply it in your real-world projects. What you will learnUnderstand browser automation fundamentalsExplore end-to-end testing with Puppeteer and its best practicesApply CSS Selectors and XPath expressions to web automationDiscover how you can leverage the power of web automation as a developerEmulate different use cases of Puppeteer such as network speed tests and geolocationGet to grips with techniques and best practices for web scraping and web content generationWho this book is for The book is for QA engineers, testing professionals, and frontend web developers alike who want to perform end-to-end testing using Google's developer tools. Web developers who want to learn how to use Puppeteer for generating content, scraping websites, and evaluating website performance will also find this book useful. Although knowledge of Node.js isn’t necessary, basic JavaScript knowledge will assist with understanding the concepts covered.
Proceedings Of Ninth International Congress On Information And Communication Technology
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Author : Xin-She Yang
language : en
Publisher: Springer Nature
Release Date : 2024-08-09
Proceedings Of Ninth International Congress On Information And Communication Technology written by Xin-She Yang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-09 with Technology & Engineering categories.
This book gathers selected high-quality research papers presented at the Ninth International Congress on Information and Communication Technology, held in London, on February 19–22, 2024. It discusses emerging topics pertaining to information and communication technology (ICT) for managerial applications, e-governance, e-agriculture, e-education and computing technologies, the Internet of Things (IoT), and e-mining. Written by respected experts and researchers working on ICT, the book offers an asset for young researchers involved in advanced studies. The work is presented in ten volumes.
Information And Communication Technologies In Education Research And Industrial Applications
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Author : Grigoris Antoniou
language : en
Publisher: Springer Nature
Release Date : 2023-11-30
Information And Communication Technologies In Education Research And Industrial Applications written by Grigoris Antoniou and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-30 with Education categories.
This book constitutes the proceedings of the 18th International Conference, ICTERI 2023, held in Ivano-Frankivsk, Ukraine, during September 18–22, 2023. The 21 full papers included in this volume were carefully reviewed and selected from 90 submissions. The volume focuses on research advances in ICT, business or academic applications of ICT, and design and deployment of ICT infrastructures.
Using Fundamental Analysis And An Ensemble Of Classifier Models Along With A Risk Off Filter To Select Outperforming Companies
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Author : Manuel Moura
language : en
Publisher: Springer Nature
Release Date : 2024-06-18
Using Fundamental Analysis And An Ensemble Of Classifier Models Along With A Risk Off Filter To Select Outperforming Companies written by Manuel Moura and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-18 with Computers categories.
This book develops a quantitative stock market investment methodology using financial indicators that beats the benchmark of S&P500 index. To achieve this goal, an ensemble of machine learning models is meticulously constructed, incorporating four distinct algorithms: support vector machine, k-nearest neighbors, random forest, and logistic regression. These models all make use of financial ratios extracted from company financial statements for the purposes of predictive forecasting. The ensemble classifier is subject to a strict testing of precision which compares it to the performance of its constituent models separately. Rolling window and cross-validation tests are used in this evaluation in order to provide a comprehensive assessment framework. A risk-off filter is developed to limit risk during uncertain market periods, and consequently to improve the Sharpe ratio of the model. The risk adjusted performance of the final model, supported by the risk-off filter, achieves a Sharpe ratio of 1.63 which surpasses both the model’s performance without the filter that delivers Sharpe ratio of 1.41 and the one from the S&P500 index of 0.80. The substantial increase in risk-adjusted returns is accomplished by reducing the model’s volatility from an annual standard of deviation of 15.75% to 11.22%, which represents an almost 30% decrease in volatility.
Big Data Cloud Computing And Data Science Engineering
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Author : Roger Lee
language : en
Publisher: Springer
Release Date : 2019-07-30
Big Data Cloud Computing And Data Science Engineering written by Roger Lee and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-30 with Computers categories.
This edited book presents the scientific outcomes of the 4th IEEE/ACIS International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD 2019) which was held on May 29–31, 2019 in Honolulu, Hawaii. The aim of the conference was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Presenting 15 of the conference’s most promising papers, the book discusses all aspects (theory, applications and tools) of computer and information science, the practical challenges encountered along the way, and the solutions adopted to solve them.
Hands On Python Deep Learning For The Web
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Author : Anubhav Singh
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-05-15
Hands On Python Deep Learning For The Web written by Anubhav Singh and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-15 with Computers categories.
Use the power of deep learning with Python to build and deploy intelligent web applications Key FeaturesCreate next-generation intelligent web applications using Python libraries such as Flask and DjangoImplement deep learning algorithms and techniques for performing smart web automationIntegrate neural network architectures to create powerful full-stack web applicationsBook Description When used effectively, deep learning techniques can help you develop intelligent web apps. In this book, you'll cover the latest tools and technological practices that are being used to implement deep learning in web development using Python. Starting with the fundamentals of machine learning, you'll focus on DL and the basics of neural networks, including common variants such as convolutional neural networks (CNNs). You'll learn how to integrate them into websites with the frontends of different standard web tech stacks. The book then helps you gain practical experience of developing a deep learning-enabled web app using Python libraries such as Django and Flask by creating RESTful APIs for custom models. Later, you'll explore how to set up a cloud environment for deep learning-based web deployments on Google Cloud and Amazon Web Services (AWS). Next, you'll learn how to use Microsoft's intelligent Emotion API, which can detect a person's emotions through a picture of their face. You'll also get to grips with deploying real-world websites, in addition to learning how to secure websites using reCAPTCHA and Cloudflare. Finally, you'll use NLP to integrate a voice UX through Dialogflow on your web pages. By the end of this book, you'll have learned how to deploy intelligent web apps and websites with the help of effective tools and practices. What you will learnExplore deep learning models and implement them in your browserDesign a smart web-based client using Django and FlaskWork with different Python-based APIs for performing deep learning tasksImplement popular neural network models with TensorFlow.jsDesign and build deep web services on the cloud using deep learningGet familiar with the standard workflow of taking deep learning models into productionWho this book is for This deep learning book is for data scientists, machine learning practitioners, and deep learning engineers who are looking to perform deep learning techniques and methodologies on the web. You will also find this book useful if you’re a web developer who wants to implement smart techniques in the browser to make it more interactive. Working knowledge of the Python programming language and basic machine learning techniques will be beneficial.
Hands On Transfer Learning With Python
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Author : Dipanjan Sarkar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-08-31
Hands On Transfer Learning With Python written by Dipanjan Sarkar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-31 with Computers categories.
Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem Key Features Build deep learning models with transfer learning principles in Python implement transfer learning to solve real-world research problems Perform complex operations such as image captioning neural style transfer Book Description Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems. What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functions Get to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLP Who this book is for Hands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.
Hands On Deep Learning With Python
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Author : Rogers Isaacson
language : en
Publisher: Independently Published
Release Date : 2025-04-14
Hands On Deep Learning With Python written by Rogers Isaacson and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-14 with Computers categories.
Unlock the world of deep learning with Hands-On Deep Learning with Python. This in-depth guide will teach you how to build, train, and optimize neural networks, convolutional networks, and recurrent networks for real-world applications using Python. Whether you're a beginner looking to break into the world of AI or an experienced developer seeking to deepen your knowledge of deep learning techniques, this book provides step-by-step instructions and practical examples to help you implement cutting-edge models. Python, along with powerful libraries like TensorFlow, Keras, and PyTorch, is the most popular ecosystem for deep learning. This book will show you how to use these libraries to build state-of-the-art neural networks for a wide range of applications, from image classification and object detection to natural language processing and time-series forecasting. Inside, you'll learn: The fundamentals of deep learning, including what neural networks are, how they work, and the different types of networks (e.g., feedforward, convolutional, and recurrent) How to set up and use popular Python libraries for deep learning, such as TensorFlow, Keras, and PyTorch The principles behind training neural networks, including backpropagation, optimization algorithms, and loss functions How to build and train Convolutional Neural Networks (CNNs) for image recognition, classification, and segmentation tasks The basics of Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs) for sequential data like text, speech, and time-series forecasting Advanced deep learning techniques, including transfer learning, data augmentation, and hyperparameter tuning How to evaluate model performance using metrics such as accuracy, precision, recall, and confusion matrices How to deploy deep learning models into production for real-time use Real-world case studies and projects that help you apply deep learning to various domains like healthcare, finance, and entertainment By the end of this book, you'll have the skills to implement advanced deep learning models using Python and apply them to solve practical problems. Hands-On Deep Learning with Python will empower you to tackle challenges in AI and machine learning and start building your own deep learning applications. Key Features: Step-by-step guidance for building neural networks, CNNs, and RNNs Hands-on projects using real-world datasets to practice and reinforce your learning Learn to implement deep learning techniques using Python libraries like TensorFlow, Keras, and PyTorch Advanced deep learning techniques like transfer learning, hyperparameter tuning, and model evaluation Practical advice for deploying deep learning models into real-world applications Start your deep learning journey today with Hands-On Deep Learning with Python and learn how to build, train, and deploy state-of-the-art neural networks for real-world problems.