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Название: Neural Networks and Deep Learning: A Textbook, 2nd Edition
Автор: Charu C. Aggarwal
Издательство: Springer
Год: 2023
Страниц: 541
Язык: английский
Формат: pdf (true), epub
Размер: 46.8 MB

Neural networks were developed to simulate the human nervous system for Machine Learning tasks by treating the computational units in a learning model in a manner similar to human neurons. Neural networks were developed soon after the advent of computers in the fifties and sixties. Rosenblatt’s perceptron algorithm was seen as a fundamental cornerstone of neural networks, which caused an initial period of euphoria — it was soon followed by disappointment as the initial successes were somewhat limited. Eventually, at the turn of the century, greater data availability and increasing computational power lead to increased successes of neural networks, and this area was reborn under the new label of “Deep Learning.” Although we are still far from the day that Artificial Intelligence (AI) is close to human performance, there are specific domains like image recognition, self-driving cars, and game playing, where AI has matched or exceeded human performance. It is also hard to predict what AI might be able to do in the future. For example, few computer vision experts would have thought two decades ago that any automated system could ever perform an intuitive task like categorizing an image more accurately than a human.

Название: Amazon Stock Price: Visualization, Forecasting, and Prediction Using Machine Learning with Python GUI, Second Edition
Автор: Vivian Siahaan, Rismon Hasiholan Sianipar
Издательство: Balige Publishing
Год: 2023
Страниц: 355
Язык: английский
Формат: epub
Размер: 21.0 MB

The Machine Learning models used predict Amazon stock daily returns as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, MLP classifier, and Extra Trees classifier. Finally, you will develop GUI to plot boundary decision, distribution of features, feature importance, predicted values versus true values, confusion matrix, learning curve, performance of the model, and scalability of the model.

Название: Anatomy of Deep Learning Principles: Writing a Deep Learning Library from Scratch
Автор: Hongwei Dong
Издательство: Independently published
Год: 2023
Страниц: 664
Язык: английский
Формат: epub
Размер: 22.1 MB

This book introduces the basic principles and implementation process of Deep Learning in a simple way, and uses Python's Numpy library to build its own Deep Learning library from scratch instead of using existing Deep Learning libraries. On the basis of introducing basic knowledge of Python programming, calculus, and probability statistics, the core basic knowledge of Deep Learning such as regression model, neural network, convolutional neural network, recurrent neural network, and generative network is introduced in sequence according to the development of Deep Learning. While analyzing the principle in a simple way, it provides a detailed code implementation process. It is like not teaching you how to use weapons and mobile phones, but teaching you how to make weapons and mobile phones by yourself. This book is not a tutorial on the use of existing Deep Learning libraries, but an analysis of how to develop Deep Learning libraries from 0. This method of combining the principle from 0 with code implementation can enable readers to better understand the basic principles of Deep Learning and the design ideas of popular Deep Learning libraries.

Название: Тайная жизнь программ. Как создать код, который понравится вашему компьютеру
Автор: Стейнхарт Джонатан
Издательство: Питер
Год: 2023
Формат: pdf
Страниц: 528
Размер: 13,1 Мб
Язык: русский

Знакомы ли вы с технологиями, лежащими в основе вашей собственной программы? Почему «правильный» код не хочет работать? Истина проста и банальна — нужно сразу создавать код, который будет работать хорошо и не будет прятать в себе трудноуловимые ошибки. Для этого Джонатан Стейнхарт исследует фундаментальные концепции, лежащие в основе работы компьютеров. Он рассматривает аппаратное обеспечение, поведение программ на определенных устройствах, чтобы показать, как на самом деле должен работать ваш код. Узнайте, что на самом деле происходит, когда вы запускаете код на компьютере, — и вы научитесь программировать лучше и эффективнее.

Название: Linux Shell Programming: Pocket Primer
Автор: Oswald Campesato
Издательство: Mercury Learning and Information
Год: 2023
Страниц: 273
Язык: английский
Формат: pdf (true), epub (true)
Размер: 18.2 MB

The goal of this book is to introduce readers to an assortment of powerful command line utilities that can be combined to create simple, yet powerful shell scripts. While all examples and scripts use the “bash” command set, many of the concepts translate into other forms of shell scripting (ksh, sh, csh), including the concept of piping data between commands, regular expression substitution and the sed and awk commands. Aimed at a reader relatively new to working in a bash environment, the book is comprehensive enough to be a good reference and teach a few new tricks to those who already have some experience with creating shells scripts. The book features companion files with code samples from the book (available with Amazon proof of purchase for free downloading from the publisher). This short book contains a variety of code fragments and shell scripts for data scientists, data analysts, and other people who want shell-based solutions to “clean” various types of datasets. In addition, the concepts and code samples in this book are useful for people who want to simplify routine tasks.

Название: R for Data Analysis in easy steps, 2nd edition
Автор: Mike McGrath
Издательство: In Easy Steps Limited
Год: 2023
Страниц: 121
Язык: английский
Формат: epub
Размер: 18.97 MB

The R language is widely used by statisticians for data analysis, and the popularity of R programming has therefore increased substantially in recent years. The emerging Internet of Things (IoT) gathers increasing amounts of data that can be analyzed to gain useful insights into trends. R for Data Analysis in easy steps, 2nd edition has an easy-to-follow style that will appeal to anyone who wants to produce graphic visualizations to gain insights from gathered data. The book begins by explaining core programming principles of the R programming language, which stores data in “vectors” from which simple graphs can be plotted. Next, it describes how to create “matrices” to store and manipulate data from which graphs can be plotted to provide better insights. This book then demonstrates how to create “data frames” from imported data sets, and how to employ the “Grammar of Graphics” to produce advanced visualizations that can best illustrate useful insights from your data. R for Data Analysis in easy steps, 2nd edition contains separate chapters on the major features of the R programming language. There are complete example programs that demonstrate how to create Line graphs, Bar charts, Histograms, Scatter graphs, Box plots, and more. The code for each R script is listed, together with screenshots that illustrate the actual output when that script has been executed. The free, downloadable example R code is provided for clearer understanding.

Название: The Well-Grounded Python Developer: How the pros use Python and Flask (Final Release)
Автор: Doug Farrel
Издательство: Manning Publications
Год: 2023
Страниц: 298
Язык: английский
Формат: pdf (true)
Размер: 10.0 MB

If you’re new to Python, it can be tough to understand when, where, and how to use all its language features. This friendly guide shows you how the Python ecosystem fits together, and grounds you in the skills you need to continue your journey to being a software developer. The Well-Grounded Python Developer builds on Python skills you’ve learned in isolation and shows you how to unify them into a meaningful whole. It helps you understand the dizzying array of libraries and teaches important concepts, like modular construction, APIs, and the design of a basic web server. As you work through this practical guide, you’ll discover how all the bits of Python link up as you build and modify a typical web server application—the kind of web app that’s in high demand by modern businesses. As a new programmer, you’re happy just to see your code run. A professional developer, on the other hand, needs to create software that runs reliably. It must be fast, maintainable, scalable, secure, well designed and documented, easy for others to update, and quick to ship. This book teaches you the skills you need to go from Python programmer to Python developer. For experienced beginners who want to learn professional-level skills.

Название: Designing Deep Learning Systems: A software engineer's guide (Final Release)
Автор: Chi Wang, Donald Szeto
Издательство: Manning Publications
Год: 2023
Страниц: 362
Язык: английский
Формат: pdf (true)
Размер: 14.1 MB

A vital guide to building the platforms and systems that bring Deep Learning models to production. Deep Learning systems are the components and infrastructure essential to supporting a deep learning model in a production environment. Written especially for software engineers with minimal knowledge of deep learning’s design requirements, Designing Deep Learning Systems is full of hands-on examples that will help you transfer your software development skills to creating these deep learning platforms. You’ll learn how to build automated and scalable services for core tasks like dataset management, model training/serving, and hyperparameter tuning. This book is the perfect way to step into an exciting—and lucrative—career as a Deep Learning engineer. To be practically usable, a deep learning model must be built into a software platform. As a software engineer, you need a deep understanding of deep learning to create such a system. Th is book gives you that depth. For software developers and engineering-minded data scientists. Examples in Java and Python.

Название: Google Anthos in Action: Manage hybrid and multi-cloud Kubernetes clusters (Final Release)
Автор: Antonio Gulli
Издательство: Manning Publications
Год: 2023
Страниц: 498
Язык: английский
Формат: pdf (true)
Размер: 24.7 MB

Learn multicloud deployment on Anthos directly from the Google development team! Anthos delivers a consistent management platform for deploying and operating Linux and Windows applications anywhere—multi-cloud, edge, on-prem, bare metal, or VMware. Google Anthos in Action demystifies Anthos with practical examples of Anthos at work and invaluable insights from the Google team that built it. You’ll learn how to use this modern, Kubernetes-based cloud platform to balance costs, automate security, and run your software literally anywhere. The book is full of Google-tested patterns that will boost efficiency across the development lifecycle. It’s an absolutely essential guide for anyone working with Anthos, or delivering software in a cloud-centric world. The operations nightmare: modern applications run on-prem, in the cloud, at the edge, on bare metal, in containers, over VMs, in any combination. And you’re expected to handle the rollouts, dataOps, security, performance, scaling, backup, and whatever else comes your way. Google Anthos feels your pain. This Kubernetes-based system simplifies hybrid and multicloud operations, providing a single platform for deploying and managing your applications, wherever they live.

Название: Evolutionary Deep Learning: Genetic algorithms and neural networks (Final Release)
Автор: Micheal Lanham
Издательство: Manning Publications
Год: 2023
Страниц: 362
Язык: английский
Формат: pdf (true)
Размер: 49.6 MB

Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning’s common pitfalls and deliver adaptable model upgrades without constant manual adjustment. Evolutionary Deep Learning is a guide to improving your Deep Learning models with AutoML enhancements based on the principles of biological evolution. This exciting new approach utilizes lesser-known AI approaches to boost performance without hours of data annotation or model hyperparameter tuning. In this one-of-a-kind guide, you’ll discover tools for optimizing everything from data collection to your network architecture. For data scientists who know Python.