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Zarządzanie
Podcasty
KNIME Essentials. Perform accurate data analysis using the power of KNIME
KNIME is an open source data analytics, reporting, and integration platform, which allows you to analyze a small or large amount of data without having to reach out to programming languages like R.KNIME Essentials teaches you all you need to know to start processing your first data sets using KNIME. It covers topics like installation, data processing, and data visualization including the KNIME reporting features. Data processing forms a fundamental part of KNIME, and KNIME Essentials ensures that you are fully comfortable with this aspect of KNIME before showing you how to visualize this data and generate reports.KNIME Essentials guides you through the process of the installation of KNIME through to the generation of reports based on data. The main parts between these two phases are the data processing and the visualization. The KNIME variants of data analysis concepts are introduced, and after the configuration and installation description comes the data processing which has many options to convert or extend it. Visualization makes it easier to get an overview for parts of the data, while reporting offers a way to summarize them in a nice way.
Applied Deep Learning with Keras. Solve complex real-life problems with the simplicity of Keras
Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme
Though designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code.Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the book guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You’ll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you’ll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you’ll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you’ll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model.By the end of this book, you will have the skills you need to use Keras when building high-level deep neural networks.
Apache Arrow is designed to accelerate analytics and allow the exchange of data across big data systems easily.In-Memory Analytics with Apache Arrow begins with a quick overview of the Apache Arrow format, before moving on to helping you to understand Arrow’s versatility and benefits as you walk through a variety of real-world use cases. You'll cover key tasks such as enhancing data science workflows with Arrow, using Arrow and Apache Parquet with Apache Spark and Jupyter for better performance and hassle-free data translation, as well as working with Perspective, an open source interactive graphical and tabular analysis tool for browsers. As you advance, you'll explore the different data interchange and storage formats and become well-versed with the relationships between Arrow, Parquet, Feather, Protobuf, Flatbuffers, JSON, and CSV. In addition to understanding the basic structure of the Arrow Flight and Flight SQL protocols, you'll learn about Dremio’s usage of Apache Arrow to enhance SQL analytics and discover how Arrow can be used in web-based browser apps. Finally, you'll get to grips with the upcoming features of Arrow to help you stay ahead of the curve.By the end of this book, you will have all the building blocks to create useful, efficient, and powerful analytical services and utilities with Apache Arrow.
Matt Harrison, Theodore Petrou
The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter.This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results.
With deep reinforcement learning, you can build intelligent agents, products, and services that can go beyond computer vision or perception to perform actions. TensorFlow 2.x is the latest major release of the most popular deep learning framework used to develop and train deep neural networks (DNNs). This book contains easy-to-follow recipes for leveraging TensorFlow 2.x to develop artificial intelligence applications.Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers OpenAI Gym, model-based RL, model-free RL, and how to develop basic agents. You'll discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, and deep recurrent Q-networks for training your RL agents. As you advance, you’ll explore the applications of reinforcement learning by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Finally, you'll find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps using TensorFlow 2.x.By the end of this TensorFlow book, you'll have gained a solid understanding of deep reinforcement learning algorithms and their implementations from scratch.
Amazon Web Services is an Infrastructure as a Service (IaaS) platform in the Cloud, which businesses can take advantage of as their needs demand. The Amazon Cloud provides the enterprise with the flexibility to choose whichever solution is required to solve specific problems, ultimately reducing costs by only paying for what you use.While enterprises understand moving their applications among infrastructure they own and manage, the differences in Amazon's infrastructure bring up specific business, legal, technical, and regulatory issues to get to grips with.This step-by-step guide to moving your Enterprise .NET application to Amazon covers not only the concept, technical design, and strategy, but also enlightens readers about the business strategy and in-depth implementation details involved in moving an application to Amazon. You'll discover how to map your requirements against the Amazon Cloud, as well as secure and enhance your application with AWS.This book helps readers achieve their goal of migrating a .NET Enterprise Application to the AWS cloud. It guides you through the process one step at a time with a sample enterprise application migration. After comparing the existing application with the newly migrated version, it then moves on to explain how to make the hosted application better. It covers how to leverage some of the scalability and redundancy built into the Cloud, and along the way you'll learn about all of the major AWS products like EC2, S3, and EBS.
Learn LLVM 12. A beginner's guide to learning LLVM compiler tools and core libraries with C++
LLVM was built to bridge the gap between compiler textbooks and actual compiler development. It provides a modular codebase and advanced tools which help developers to build compilers easily. This book provides a practical introduction to LLVM, gradually helping you navigate through complex scenarios with ease when it comes to building and working with compilers.You’ll start by configuring, building, and installing LLVM libraries, tools, and external projects. Next, the book will introduce you to LLVM design and how it works in practice during each LLVM compiler stage: frontend, optimizer, and backend. Using a subset of a real programming language as an example, you will then learn how to develop a frontend and generate LLVM IR, hand it over to the optimization pipeline, and generate machine code from it. Later chapters will show you how to extend LLVM with a new pass and how instruction selection in LLVM works. You’ll also focus on Just-in-Time compilation issues and the current state of JIT-compilation support that LLVM provides, before finally going on to understand how to develop a new backend for LLVM.By the end of this LLVM book, you will have gained real-world experience in working with the LLVM compiler development framework with the help of hands-on examples and source code snippets.