Realistic Asset Creation with Adobe Substance 3D. Create materials, textures, filters, and 3D models using Substance 3D Painter, Designer, and Stager

Zeeshan Jawed Shah

Adobe Substance 3D is a comprehensive suite complete with everything an artist needs to create stunning 3D digital materials. Getting a grip on the ecosystem of apps can be challenging for beginners, which is where Realistic Asset Creation with Adobe Substance 3D comes in!This practical guide doesn't bombard you with reams of textual information. Instead, you get an interactive, project-based book that’ll help you gain sound knowledge of Adobe Substance 3D and set you on the right path toward a career in 3D design. You’ll start off with the rudiments of Adobe Substance 3D Painter, which will enable you to acquire the skills needed to work with layers, masks, shelves, textures, and more. Next, you’ll move on to Adobe Substance 3D Designer and become well-acquainted with this node-based design tool as you progress through the chapters. The final section is devoted to Adobe Substance 3D Stager, which teaches you to build complex 3D scenes and visualize your edits in real time.By the end of this Adobe book, you’ll have gained a solid understanding of Adobe Substance 3D and developed the skills to build a comprehensive portfolio of work, setting you up for a lucrative career in 3D design.

Learning Robotics Using Python. Bring robotics projects to life with Python! Discover how to harness everything from Blender to ROS and OpenCV with one of our most popular robotics books

Marek Suppa, Lentin Joseph

Learning Predictive Analytics with Python. Click here to enter text

Ashish Kumar

Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form - It needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age. This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy. You’ll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world.

Machine Learning: End-to-End guide for Java developers. Data Analysis, Machine Learning, and Neural Networks simplified

Boštjan Kaluža, Krishna Choppella, Uday Kamath

Machine Learning is one of the core area of Artificial Intelligence where computers are trained to self-learn, grow, change, and develop on their own without being explicitly programmed. In this course, we cover how Java is employed to build powerful machine learning models to address the problems being faced in the world of Data Science. The course demonstrates complex data extraction and statistical analysis techniques supported by Java, applying various machine learning methods, exploring machine learning sub-domains, and exploring real-world use cases such as recommendation systems, fraud detection, natural language processing, and more, using Java programming. The course begins with an introduction to data science and basic data science tasks such as data collection, data cleaning, data analysis, and data visualization. The next section has a detailed overview of statistical techniques, covering machine learning, neural networks, and deep learning. The next couple of sections cover applying machine learning methods using Java to a variety of chores including classifying, predicting, forecasting, market basket analysis, clustering stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, and deep learning.The last section highlights real-world test cases such as performing activity recognition, developing image recognition, text classification, and anomaly detection. The course includes premium content from three of our most popular books:[*]Java for Data Science[*]Machine Learning in Java [*]Mastering Java Machine LearningOn completion of this course, you will understand various machine learning techniques, different machine learning java algorithms you can use to gain data insights, building data models to analyze larger complex data sets, and incubating applications using Java and machine learning algorithms in the field of artificial intelligence.

Mastering Machine Learning Algorithms. Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work - Second Edition

Giuseppe Bonaccorso

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains.You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks.By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.

Managing Data Science. Effective strategies to manage data science projects and build a sustainable team

Kirill Dubovikov

Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis.

Unity Virtual Reality Projects. Learn Virtual Reality by developing more than 10 engaging projects with Unity 2018 - Second Edition

Jonathan Linowes

Unity has become the leading platform for building virtual reality games, applications, and experiences for this new generation of consumer VR devices. Unity Virtual Reality Projects walks you through a series of hands-on tutorials and in-depth discussions on using the Unity game engine to develop VR applications. With its practical and project-based approach, this book will get you up to speed with the specifics of VR development in Unity. You will learn how to use Unity to develop VR applications that can be experienced with devices such as Oculus, Daydream, and Vive. Among the many topics and projects, you will explore gaze-based versus hand-controller input, world space UI canvases, locomotion and teleportation, software design patterns, 360-degree media, timeline animation, and multiplayer networking. You will learn about the Unity 3D game engine via the interactive Unity Editor, and you will also learn about C# programming. By the end of the book, you will be fully equipped to develop rich, interactive VR experiences using Unity.

The MVVM Pattern in .NET MAUI. The definitive guide to essential patterns, best practices, and techniques for cross-platform app development

Pieter Nijs, David Ortinau

In today's fast-paced world of modern software development, teams need to be efficient, productive, and capable of rapidly adapting to changes to deliver high-quality products, making it crucial for developers to write maintainable and easy-to-test code.The MVVM Pattern in .NET MAUI helps you to thoroughly explore the Model-View-View Model (MVVM) design pattern. The chapters show you how this pattern helps in structuring code to embrace the separation of concerns, allowing for loosely coupled user interface and application logic, which ultimately empowers you to write more robust, maintainable, and testable code. The book also highlights .NET MAUI's capabilities and features, and enables you to delve into the essential components within the framework that facilitate the application of the MVVM pattern. With the help of a sample application, this definitive guide takes a hands-on approach to walk you through both the essential and advanced usages of the MVVM pattern to ensure that you successfully apply the practical aspects of the pattern to your .NET MAUI projects.By the end of this book, you’ll have gained a comprehensive understanding of the MVVM design pattern and its relevance in the context of .NET MAUI, as well as developed the skills needed to successfully apply it in practice.