Programowanie
Alessandro Marrandino
BigQuery ML enables you to easily build machine learning (ML) models with SQL without much coding. This book will help you to accelerate the development and deployment of ML models with BigQuery ML.The book starts with a quick overview of Google Cloud and BigQuery architecture. You'll then learn how to configure a Google Cloud project, understand the architectural components and capabilities of BigQuery, and find out how to build ML models with BigQuery ML. The book teaches you how to use ML using SQL on BigQuery. You'll analyze the key phases of a ML model's lifecycle and get to grips with the SQL statements used to train, evaluate, test, and use a model. As you advance, you'll build a series of use cases by applying different ML techniques such as linear regression, binary and multiclass logistic regression, k-means, ARIMA time series, deep neural networks, and XGBoost using practical use cases. Moving on, you'll cover matrix factorization and deep neural networks using BigQuery ML's capabilities. Finally, you'll explore the integration of BigQuery ML with other Google Cloud Platform components such as AI Platform Notebooks and TensorFlow along with discovering best practices and tips and tricks for hyperparameter tuning and performance enhancement.By the end of this BigQuery book, you'll be able to build and evaluate your own ML models with BigQuery ML.
Michael Bironneau, Toby Coleman
Machine learning is an essential part of today's data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology. This book will teach you how to efficiently develop machine learning applications in Go.The book starts with an introduction to machine learning and its development process, explaining the types of problems that it aims to solve and the solutions it offers. It then covers setting up a frictionless Go development environment, including running Go interactively with Jupyter notebooks. Finally, common data processing techniques are introduced.The book then teaches the reader about supervised and unsupervised learning techniques through worked examples that include the implementation of evaluation metrics. These worked examples make use of the prominent open-source libraries GoML and Gonum.The book also teaches readers how to load a pre-trained model and use it to make predictions. It then moves on to the operational side of running machine learning applications: deployment, Continuous Integration, and helpful advice for effective logging and monitoring.At the end of the book, readers will learn how to set up a machine learning project for success, formulating realistic success criteria and accurately translating business requirements into technical ones.
Brett Lantz
Dive into R with this data science guide on machine learning (ML). Machine Learning with R, Fourth Edition, takes you through classification methods like nearest neighbor and Naive Bayes and regression modeling, from simple linear to logistic.Dive into practical deep learning with neural networks and support vector machines and unearth valuable insights from complex data sets with market basket analysis. Learn how to unlock hidden patterns within your data using k-means clustering.With three new chapters on data, you’ll hone your skills in advanced data preparation, mastering feature engineering, and tackling challenging data scenarios. This book helps you conquer high-dimensionality, sparsity, and imbalanced data with confidence. Navigate the complexities of big data with ease, harnessing the power of parallel computing and leveraging GPU resources for faster insights.Elevate your understanding of model performance evaluation, moving beyond accuracy metrics. With a new chapter on building better learners, you’ll pick up techniques that top teams use to improve model performance with ensemble methods and innovative model stacking and blending techniques.Machine Learning with R, Fourth Edition, equips you with the tools and knowledge to tackle even the most formidable data challenges. Unlock the full potential of machine learning and become a true master of the craft.
Quan Hua, Saif Ahmed, Shams Ul Azeem
Google's TensorFlow is a game changer in the world of machine learning. It has made machine learning faster, simpler, and more accessible than ever before. This book will teach you how to easily get started with machine learning using the power of Python and TensorFlow 1.x. Firstly, you’ll cover the basic installation procedure and explore the capabilities of TensorFlow 1.x. This is followed by training and running the first classifier, and coverage of the unique features of the library including data ?ow graphs, training, and the visualization of performance with TensorBoard—all within an example-rich context using problems from multiple industries. You’ll be able to further explore text and image analysis, and be introduced to CNN models and their setup in TensorFlow 1.x. Next, you’ll implement a complete real-life production system from training to serving a deep learning model. As you advance you’ll learn about Amazon Web Services (AWS) and create a deep neural network to solve a video action recognition problem. Lastly, you’ll convert the Caffe model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-Slim.By the end of this book, you will be geared up to take on any challenges of implementing TensorFlow 1.x in your machine learning environment.
Rich Collier, Camilla Montonen, Bahaaldine Azarmi
Elastic Stack, previously known as the ELK stack, is a log analysis solution that helps users ingest, process, and analyze search data effectively. With the addition of machine learning, a key commercial feature, the Elastic Stack makes this process even more efficient. This updated second edition of Machine Learning with the Elastic Stack provides a comprehensive overview of Elastic Stack's machine learning features for both time series data analysis as well as for classification, regression, and outlier detection.The book starts by explaining machine learning concepts in an intuitive way. You'll then perform time series analysis on different types of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you'll deploy machine learning within Elastic Stack for logging, security, and metrics. Finally, you'll discover how data frame analysis opens up a whole new set of use cases that machine learning can help you with.By the end of this Elastic Stack book, you'll have hands-on machine learning and Elastic Stack experience, along with the knowledge you need to incorporate machine learning in your distributed search and data analysis platform.
Machines That Think. How Artificial Intelligence Works and What It Means for Us
Rheinwerk Publishing, Inc, Inga Strümke
Machines That Think explores the evolution of artificial intelligence (AI), from its roots in early theoretical frameworks to modern machine learning technologies. It begins by diving into the history of AI, featuring the foundational contributions of pioneers like Alan Turing and John von Neumann. The book then examines the attempts to make machines intelligent, covering symbolic AI, expert systems, and the rise of neural networks. With a focus on the technological advancements that shaped AI, this book provides readers with a deep understanding of how AI systems have evolved over time.The book continues by addressing the growing importance of data in AI systems. It explores how data shapes machine learning models and the inherent challenges faced by data scientists when gathering and processing data for AI applications. The impact of data on model accuracy and the ethical dilemmas surrounding its collection and usage are also discussed. In the final chapters, the book delves into the ethical and societal implications of AI, exploring issues such as privacy, accountability, and the future of AI in everyday life. It speculates on the future of artificial general intelligence (AGI) and superintelligence, contemplating the potential consequences of these technologies.
Magento Extensions Development. Click here to enter text
Jérémie Bouchet
Magento has been revealed as the best and the most popular open source e-commerce platform in the world, with about 250k+ online stores. Magento 2 is the most recent version of this awesome toolset: every new and modern development techniques are used to offer a real modular approach and powerful architecture. The book will support you in the writing of innovative and complex extensions. Starting from the beginning, we will cover how to set up a development environment that allows you to be really efficient in your functionality writing, including GIT registering and many other development tools. We then move on to provide a large overview of the best practices to scale your module in a high-load environment. After these foundations, you will see how to use test driven-development (TDD) and unit tests to handle your code. We then build a complex extension together, step by step, and internationally-ready. Next, you will find out how to protect the users’ data. Finally, we will take a look a publishing the extension on the new Magento Connect marketplace and how to protect your intellectual property.After you read this book, you will know everything you need to know to become an invaluable extension editor, whether it is for your customers’ needs or for your own requirements.
Makra i VBA w tydzień. Odkryj potęgę programowania!
Mateusz Boryga
Nauka programowania dla nieprogramistów Jeśli zastanawiasz się nad przekwalifikowaniem i karierą w branży informatycznej albo chcesz poszerzyć swoje umiejętności o programowanie, ale wydaje Ci się ono czarną magią, zapewniamy - w programowaniu nie ma nic z magii. To proces polegający na tworzeniu zbioru instrukcji, dzięki którym komputer wykona to, czego od niego oczekujesz. Jak się nauczyć pisać te instrukcje? Jeśli dysponujesz pakietem Microsoft Office i arkuszem kalkulacyjnym Excel, to masz też dostęp do języka programowania VBA (Visual Basic for Applications). Autor tego poradnika pokaże Ci, że programowania najłatwiej nauczyć się właśnie w VBA. Pośród innych użytecznych narzędzi bowiem Microsoft Excel posiada również rejestrator makr. To on zamienia klikane w Excelu działania na pełnoprawny kod VBA. Dzięki właściwemu użyciu rejestratora możliwe jest więc proste generowanie kodu, na którym można dalej pracować. Wygeneruj kod za pomocą rejestratora makr Excela Zamień go na uniwersalny kod, przydatny w wielu projektach Zobacz najpopularniejsze gotowe do użycia fragmenty kodu VBA Poznaj dobre praktyki programowania w VBA Przejdź na wyższy poziom kodowania i doceń, jak wiele czasu dzięki temu oszczędzasz O książce w mediach: Recenzja książki [Recenzje na widelcu]