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Classic Computer Science Problems in Swift
Classic Computer Science Problems in Swift

Don't just learn another language. Become a better programmer instead. Today's awesome iOS apps stand on the shoulders of classic algorithms, coding techniques, and engineering principles. Master these core skills in Swift, and you'll be ready for AI, data-centric programming, machine learning, and the other development challenges that will define the next decade. Classic Computer Science Problems in Swift deepens your Swift language skills by exploring foundational coding techniques and algorithms. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your "new" problems. You'll appreciate author David Kopec's amazing ability to connect the core disciplines of computer science to the real-world concerns of apps, data, performance, and even nailing your next job interview! ...
TensorFlow For Dummies
TensorFlow For Dummies

Google TensorFlow has become the darling of financial firms and research organizations, but the technology can be intimidating and the learning curve is steep. Luckily, TensorFlow For Dummies is here to offer you a friendly, easy-to-follow book on the subject. Inside, you'll find out how to write applications with TensorFlow, while also grasping the concepts underlying machine learning - all without ever losing your cool! Machine learning has become ubiquitous in modern society, and its applications include language translation, robotics, handwriting analysis, financial prediction, and image recognition. TensorFlow is Google's preeminent toolset for machine learning, and this hands-on guide makes it easy to understand, even for those without a background in artificial intelligence. Install TensorFlow on your computer; Learn the fundamentals of statistical regression and neural networks; Visualize the machine learning process with TensorBoard; Perform image recognition with convo ...
Data Science Fundamentals for Python and MongoDB
Data Science Fundamentals for Python and MongoDB

Build the foundational data science skills necessary to work with and better understand complex data science algorithms. This example-driven book provides complete Python coding examples to complement and clarify data science concepts, and enrich the learning experience. Coding examples include visualizations whenever appropriate. The book is a necessary precursor to applying and implementing machine learning algorithms. The book is self-contained. All of the math, statistics, stochastic, and programming skills required to master the content are covered. In-depth knowledge of object-oriented programming isn't required because complete examples are provided and explained. Data Science Fundamentals with Python and MongoDB is an excellent starting point for those interested in pursuing a career in data science. Like any science, the fundamentals of data science are a prerequisite to competency. Without proficiency in mathematics, statistics, data manipulation, and coding, the path ...
Python Data Science Handbook
Python Data Science Handbook

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all - IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. IPython and Jupyter: provide computational environments for data scientists using Python; NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python; Pandas: features the DataFrame for efficient stora ...
Splunk Operational Intelligence Cookbook, 3rd Edition
Splunk Operational Intelligence Cookbook, 3rd Edition

Splunk makes it easy for you to take control of your data, and with Splunk Operational Cookbook, you can be confident that you are taking advantage of the Big Data revolution and driving your business with the cutting edge of operational intelligence and business analytics. With more than 70 recipes that demonstrate all of Splunk's features, not only will you find quick solutions to common problems, but you'll also learn a wide range of strategies and uncover new ideas that will make you rethink what operational intelligence means to you and your organization. You'll discover recipes on data processing, searching and reporting, dashboards, and visualizations to make data shareable, communicable, and most importantly meaningful. You'll also find step-by-step demonstrations that walk you through building an operational intelligence application containing vital features essential to understanding data and to help you successfully integrate a data-driven way of thinking in your organ ...
Intelligent Mobile Projects with TensorFlow
Intelligent Mobile Projects with TensorFlow

As a developer, you always need to keep an eye out and be ready for what will be trending soon, while also focusing on what's trending currently. So, what's better than learning about the integration of the best of both worlds, the present and the future? Artificial Intelligence (AI) is widely regarded as the next big thing after mobile, and Google's TensorFlow is the leading open source machine learning framework, the hottest branch of AI. This book covers more than 10 complete iOS, Android, and Raspberry Pi apps powered by TensorFlow and built from scratch, running all kinds of cool TensorFlow models offline on-device: from computer vision, speech and language processing to generative adversarial networks and AlphaZero-like deep reinforcement learning. You'll learn how to use or retrain existing TensorFlow models, build your own models, and develop intelligent mobile apps running those TensorFlow models. You'll learn how to quickly build such apps with step-by-step tutorials and how ...
Artificial Intelligence for Big Data
Artificial Intelligence for Big Data

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, ima ...
Metal Programming Guide
Metal Programming Guide

Metal enables Apple developers to maximize performance in demanding tasks like 3D graphics, games, scientific programming, visualization, and GPU-accelerated machine learning. Metal Programming Guide is the authoritative, practical guide to Metal for all iOS programmers who are interested in graphics programming but don't know where to start. Pioneering Apple developer Janie Clayton covers everything from basic draw calls to advanced parallel computing, combining easy-to-understand conceptual explanations with well-tested Swift 4 / Xcode 9 sample code. Clayton introduces the essential Metal, graphics, and math concepts every graphics programmer needs to know. She also discusses key graphics-specific libraries, concepts, and Metal Classes, presenting techniques and examples you'll find valuable for both graphics and data processing. Clayton also provides coverage of the Metal Compute Pipeline, demonstrating practical GPU programming applications ranging from image processing to ne ...
Hands-On Microservices with C#
Hands-On Microservices with C#

C# is a powerful language when it comes to building applications and software architecture using rich libraries and tools such as .NET. This book will harness the strength of C# in developing microservices architectures and applications. This book shows developers how to develop an enterprise-grade, event-driven, asynchronous, message-based microservice framework using C#, .NET, and various open source tools. We will discuss how to send and receive messages, how to design many types of microservice that are truly usable in a corporate environment. We will also dissect each case and explain the code, best practices, pros and cons, and more. Through our journey, we will use many open source tools, and create file monitors, a machine learning microservice, a quantitative financial microservice that can handle bonds and credit default swaps, a deployment microservice to show you how to better manage your deployments, and memory, health status, and other microservices. By the end o ...
Hands-On Convolutional Neural Networks with TensorFlow
Hands-On Convolutional Neural Networks with TensorFlow

Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips ...
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