Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform. Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments. Building Machine Learning and Deep Learning Models on Google Cloud Platform is dividedinto eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP. What You’ll Learn Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your results Know the programming concepts relevant to machine and deep learning design and development using the Python stack Build and interpret machine and deep learning models Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products Be aware of the different facets and design choices to consider when modeling a learning problem Productionalize machine learning models into software products Who This Book Is For Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers

Building Machine Learning and Deep Learning Models on Google Cloud Platform

In Detail This book covers how to develop different applications and solve recurring problems using Laravel 4 design patterns. It will walk you through the widely used design patterns--the Builder (Manager) pattern, Factory pattern, Repository pattern, and Strategy pattern--and will empower you to use these patterns while developing various applications with Laravel. This book will help you find stable and acceptable solutions, thereby improving the quality of your applications. Throughout the course of the book, you will be introduced to a number of clear, practical examples about PHP design patterns and their usage in various projects. You will also get acquainted with the best practices for Laravel that will greatly reduce the probability of introducing errors into your web applications. By the end of the book, you will be accustomed to the best practices and the important design patterns used in Laravel to make a great website. Approach This book is a practical guide packed with clear examples that will help you get to grips with the best practices in Laravel design patterns to create advanced web applications. Who this book is for This book is intended for web application developers working with Laravel who want to increase the efficiency of their web applications. It assumes that you have some experience with the Laravel PHP framework and are familiar with coding OOP methods.

Laravel Design Patterns and Best Practices

SOLID principles are an essential part of object-oriented software development and have proven to be valuable tools for developing clean, maintainable and extensible code. In industrial automation engineering, especially in programming controllers with IEC 61131-3, it is of particular importance to develop robust and reliable systems. In this book, SOLID principles are presented in detail and explained with examples in IEC 61131-3. It also illustrates how the application of these principles improves the maintainability, extensibility, and reliability of software systems. In addition to the SOLID principles, the principles KISS, DRY, LoD and YAGNI are also presented. These do not belong to the group of SOLID principles, but they are a helpful addition.

Use of the SOLID principles with the IEC 61131-3

Books, a collection of books created by Rafi Bagaskara Halilintar. With books like Building Machine Learning and Deep Learning Models on Google Cloud Platform, Laravel Design Patterns and Best Practices, Use of the SOLID principles with the IEC 61131-3