Machine Learning Design PatternsThe design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice.
In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation.
You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models; Represent data for different ML model types, including embeddings, feature crosses, and more; Choose the right model type for specific problems; Build a robust training ...
Artificial Intelligence in FinanceThe widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading.
Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book.
In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI); Understand why data-driven finance, AI, and machin ...
Advanced Analytics in Power BI with R and PythonThis easy-to-follow guide provides R and Python recipes to help you learn and apply the top languages in the field of data analytics to your work in Microsoft Power BI. Data analytics expert and author Ryan Wade shows you how to use R and Python to perform tasks that are extremely hard, if not impossible, to do using native Power BI tools. For example, you will learn to score Power BI data using custom data science models and powerful models from Microsoft Cognitive Services.
The R and Python languages are powerful complements to Power BI. They enable advanced data transformation techniques that are difficult to perform in Power BI in its default configuration but become easier by leveraging the capabilities of R and Python. If you are a business analyst, data analyst, or a data scientist who wants to push Power BI and transform it from being just a business intelligence tool into an advanced data analytics tool, then this is the book to help you do that. ...
LaTeX in 24 HoursThis book presents direct and concise explanations and examples to many LaTeX syntax and structures, allowing students and researchers to quickly understand the basics that are required for writing and preparing book manuscripts, journal articles, reports, presentation slides and academic theses and dissertations for publication.
Unlike much of the literature currently available on LaTeX, which takes a more technical stance, focusing on the details of the software itself, this book presents a user-focused guide that is concerned with its application to everyday tasks and scenarios. It is packed with exercises and looks at topics like formatting text, drawing and inserting tables and figures, bibliographies and indexes, equations, slides, and provides valuable explanations to error and warning messages so you can get work done with the least time and effort needed. This means LaTeX in 24 Hours can be used by students and researchers with little or no previous experience with LaTeX to ...
How To Code in Node.jsNode.js is a popular open-source runtime environment that can execute JavaScript outside of the browser. The Node runtime is commonly used for back-end web development, leveraging its asynchronous capabilities to create networking applications and web servers. Node is also a popular choice for building command line tools.
In this book, you will go through exercises to learn the basics of how to code in Node.js, gaining skills that apply equally to back-end and full stack development in the process.
By the end of this book you will be able to write programs that leverage Node's asynchronous code execution capabilities, complete with event emitters and listeners that will respond to user actions. Along the way you will learn how to debug Node applications using the built-in debugging utilities, as well as the Chrome browser's DevTools utilities. You will also learn how to write automated tests for your programs to ensure that any features that you add or change function as you expe ...
Certified Kubernetes Application Developer (CKAD) Study GuideDevelopers with the ability to operate, troubleshoot, and monitor applications in Kubernetes are in high demand today. To meet this need, the Cloud Native Computing Foundation created a certification exam to establish a developer's credibility and value in the job market to work in a Kubernetes environment.
The Certified Kubernetes Application Developer (CKAD) exam is different from the typical multiple-choice format of other certifications. Instead, the CKAD is a performance-based exam that requires deep knowledge of the tasks under immense time pressure.
This study guide walks you through all the topics you need to fully prepare for the exam. Author Benjamin Muschko also shares his personal experience with preparing for all aspects of the exam.
Learn when and how to apply Kubernetes concepts to manage an application; Understand the objectives, abilities, tips, and tricks needed to pass the CKAD exam; Explore the ins and outs of the kubectl command-line tool; Demonstrate comp ...
Patterns in the MachineDiscover how to apply software engineering patterns to develop more robust firmware faster than traditional embedded development approaches. In the authors' experience, traditional embedded software projects tend towards monolithic applications that are optimized for their target hardware platforms. This leads to software that is fragile in terms of extensibility and difficult to test without fully integrated software and hardware. Patterns in the Machine focuses on creating loosely coupled implementations that embrace both change and testability.
This book illustrates how implementing continuous integration, automated unit testing, platform-independent code, and other best practices that are not typically implemented in the embedded systems world is not just feasible but also practical for today's embedded projects.
After reading this book, you will have a better idea of how to structure your embedded software projects. You will recognize that while writing unit tests, creating ...
TensorFlow 2.x in the Colaboratory CloudUse TensorFlow 2.x with Google's Colaboratory (Colab) product that offers a free cloud service for Python programmers. Colab is especially well suited as a platform for TensorFlow 2.x deep learning applications. You will learn Colab's default install of the most current TensorFlow 2.x along with Colab's easy access to on-demand GPU hardware acceleration in the cloud for fast execution of deep learning models. This book offers you the opportunity to grasp deep learning in an applied manner with the only requirement being an Internet connection. Everything else - Python, TensorFlow 2.x, GPU support, and Jupyter Notebooks - is provided and ready to go from Colab.
The book begins with an introduction to TensorFlow 2.x and the Google Colab cloud service. You will learn how to provision a workspace on Google Colab and build a simple neural network application. From there you will progress into TensorFlow datasets and building input pipelines in support of modeling and testing. You will fi ...
.NET Framework Notes for ProfessionalsThe .NET Framework Notes for Professionals book is compiled from Stack Overflow Documentation, the content is written by the beautiful people at Stack Overflow. ...
Programming DSLs in KotlinCreating your own domain-specific languages (DSLs) is both challenging and exhilarating. DSLs give users a way to interact with your applications more effectively, and Kotlin is a fantastic language to serve as a host for internal DSLs, because it greatly reduces the pain and effort of design and development. But implementing DSLs on top of Kotlin requires understanding the key strengths of the language and knowing how to apply them appropriately. Learn to avoid the pitfalls and leverage the language while creating your own elegant, fluent, concise, and robust DSLs using Kotlin.
Internal DSLs remove the burdens of implementing a full blown language compiler. The host language quickly becomes your ally to creating DSLs, but the syntax you can choose for your DSLs is limited to what the host language allows. You can work around the limitations by tactfully bending the rules and exploiting the language capabilities. Learn the power of Kotlin and ways to design with it, in the context o ...