Kubernetes: Up and Running: Dive into the Future of Infrastructure
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iOS Apprentice: Beginning iOS development with Swift 4.2, 7th Edition: For Complete Beginners!: Up to date for iOS 12, Xcode 10 & Swift 4.2
In this iOS 12 programming book for beginners, you’ll learn how to create 4 complete iOS and Swift apps by following easy step-by-step tutorials. The iOS Apprentice: Beginning iOS development with Swift 4.2, 7th Edition is a series of epic-length tutorials for beginners where you’ll learn how to build 4 complete apps from scratch.
Each new app will be a little more advanced than the one before, and together they cover everything you need to know to make your own apps. By the end of the series you’ll be experienced enough to turn your ideas into real apps that you can sell on the App Store.
These tutorials have easy to follow step-by-step instructions, and consist of more than 900 pages and 500 illustrations! You also get full source code, image files, and other resources you can re-use for your own projects.
If you’re new to iOS and Swift 4, or to programming in general, learning how to write an app can seem incredibly overwhelming. The iOS Apprentice series doesn’t cover every single feature of iOS – it just focuses on the absolutely essential ones that you need to know.
Instead of just covering a list of features, this series does something much more important: it explains how all the different building blocks fit together and what is involved in building real apps.
You’re not going to create quick example programs that demonstrate how to accomplish a single feature. Instead, you’ll develop complete, fully-formed apps that are good enough to submit to the App Store!
Hands-On Unsupervised Learning with Python: Discover the skill-sets required to implement various approaches to Machine Learning with Python
Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python.
This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images.
By the end of this Hands-On Unsupervised Learning with Python book, you will have learned the art of unsupervised learning for different real-world challenges.