Big data
Ivan Vasilev
In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles.By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world.
Ivan Vasilev
In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles.By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world.
Bharatendra Rai
Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them. This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples.
Advanced Excel 365. Master Excel 365 with practical tips and advanced AI integrations
Mercury Learning and Information, Ritu Arora
Excel is more than a spreadsheet—it's a powerhouse for data analysis, decision-making, and business productivity. This course begins with foundational concepts, such as cell referencing, formulas, and functions, before moving into tools like data validation, pivot tables, and conditional formatting. You'll also explore advanced techniques for working across multiple worksheets and creating impactful charts.The journey continues with in-depth coverage of VBA programming, where you'll learn to automate repetitive tasks, debug errors, and create user-friendly applications. Starting with the basics of macros and variables, you'll advance to building custom add-ins and employing sophisticated VBA techniques to handle complex data processes seamlessly. Each lesson combines practical examples with actionable insights.By the end of the course, you'll possess a versatile toolkit to handle everything from daily spreadsheet tasks to creating powerful custom solutions. Whether you're preparing reports, analyzing trends, or developing VBA-driven applications, this course equips you to work smarter and achieve more.
John Hearty
Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano.By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering.
Cory Lesmeister, Dr. Sunil Kumar Chinnamgari
R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics. This Learning Path shows you how to leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. You’ll work through realistic projects such as building powerful machine learning models with ensembles to predict employee attrition. Next, you’ll explore different clustering techniques to segment customers using wholesale data and even apply TensorFlow and Keras-R for performing advanced computations. Each chapter will help you implement advanced machine learning algorithms using real-world examples. You’ll also be introduced to reinforcement learning along with its use cases and models. Finally, this Learning Path will provide you with a glimpse into how some of these black box models can be diagnosed and understood. By the end of this Learning Path, you’ll be equipped with the skills you need to deploy machine learning techniques in your own projects.
Advanced MySQL 8. Discover the full potential of MySQL and ensure high performance of your database
Eric Vanier, Birju Shah, Tejaswi Malepati
Advanced MySQL 8 teaches you to enhance your existing database infrastructure and build various tools to improve your enterprise applications and overall website performance. The book starts with the new and exciting MySQL 8.0 features and how to utilize them for maximum efficiency. As you make your way through the chapters, you will learn to optimize MySQL performance using indexes and advanced data query techniques for large queries. You will also discover MySQL Server 8.0 settings and work with the MySQL data dictionary to boost the performance of your database. In the concluding chapters, you will cover MySQL 8.0 Group Replication, which will enable you to create elastic, highly available, and fault-tolerant replication topologies. You will also explore backup and recovery techniques for your databases and understand important tips and tricks to help your critical data reach its full potential.By the end of this book, you’ll have learned about new MySQL 8.0 security features that allow a database administrator (DBA) to simplify user management and increase the security of their multi-user environments.
Saurabh K. Gupta
Oracle Database is one of the most popular databases and allows users to make efficient use of their resources and to enhance service levels while reducing the IT costs incurred. Oracle Database is sometimes compared with Microsoft SQL Server, however, Oracle Database clearly supersedes SQL server in terms of high availability and addressing planned and unplanned downtime. Oracle PL/SQL provides a rich platform for application developers to code and build scalable database applications and introduces multiple new features and enhancements to improve development experience. Advanced Oracle PL/SQL Developer's Guide, Second Edition is a handy technical reference for seasoned professionals in the database development space. This book starts with a refresher of fundamental concepts of PL/SQL, such as anonymous block, subprograms, and exceptions, and prepares you for the upcoming advanced concepts. The next chapter introduces you to the new features of Oracle Database 12c, not limited to PL/SQL. In this chapter, you will understand some of the most talked about features such as Multitenant and Database In-Memory. Moving forward, each chapter introduces advanced concepts with the help of demonstrations, and provides you with the latest update from Oracle Database 12c context. This helps you to visualize the pre- and post-applications of a feature over the database releases. By the end of this book, you will have become an expert in PL/SQL programming and will be able to implement advanced concepts of PL/SQL for efficient management of Oracle Database.
Advanced Splunk. Master the art of getting the maximum out of your machine data using Splunk
Ashish Kumar Tulsiram Yadav
Master the power of Splunk and learn the advanced strategies to get the most out of your machine data with this practical advanced guide. Make sense of the hidden data of your organization – the insight of your servers, devices, logs, traffic and clouds. Advanced Splunk shows you how.Dive deep into Splunk to find the most efficient solution to your data problems. Create the robust Splunk solutions you need to make informed decisions in big data machine analytics. From visualizations to enterprise integration, this well-organized high level guide has everything you need for Splunk mastery.Start with a complete overview of all the new features and advantages of the latest version of Splunk and the Splunk Environment. Go hands on with uploading data, search commands for basic and advanced analytics, advanced visualization techniques, and dashboard customizing. Discover how to tweak Splunk to your needs, and get a complete on Enterprise Integration of Splunk with various analytics and visualization tools. Finally, discover how to set up and use all the new features of the latest version of Splunk.