Cloud Infrastructure, Backend Development, and Machine Learning
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Cloud Infrastructure, Backend Development, and Machine Learning

About the Bundle

This bundle is an excellent resource for professionals working in cloud infrastructure, backend development, and machine learning.

"Terraform for Developers" provides an in-depth tutorial on the popular platform for automating cloud infrastructure. Essential reading for cloud architects and engineers, this book walks readers through the basics of Terraform as well as its advanced features and configuration.

And to round things off, "Practical C++ Backend Programming" gives a thorough explanation of how to use C++ for backend development. Building scalable and secure backend systems is covered in detail, along with topics like concurrent programming, APIs, database management, and security.

New to this bundle is the "TensorFlow Developer Certification Guide," which serves as an introduction to the widely used deep learning framework TensorFlow. Reading this book will get you ready to take the TensorFlow Developer Certification exam by covering the framework's architecture, neural network techniques, and deployment tactics.

Taken as a whole, these books fill in the gaps in our understanding of how to build, administer, and optimize complex cloud, backend, and AI applications.

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  • Categories

    • System Integration
    • Version Control
    • Software Engineering
    • Terraform
    • Infrastructure as Code
    • C and C++
    • MongoDB
    • Networking
    • APIs
    • Testing
    • Software
    • Artificial Intelligence
    • Machine Learning
    • Python

About the Books

Terraform for Developers

Essentials of Infrastructure Automation and Provisioning
  • 100%

    Complete

  • PDF

  • EPUB

"Terraform for Developers" provides a comprehensive guide to mastering infrastructure as code with Terraform, intended for both beginners and experienced practitioners. The book starts by introducing foundational Terraform concepts like its architecture, setup on various cloud platforms, and the syntax of its configuration language HCL. This grounds the reader in core skills needed to author infrastructure code.

It then explores the full lifecycle of a Terraform project from state management, provisioning, networking, troubleshooting errors, and testing practices. Each phase is explained through practical examples relevant to real-world usage. Advanced skills like performance optimization, CI/CD integration, and modules are covered to take the reader from basic to expert.

The book finishes with sophisticated use cases like Kubernetes infrastructure automation and secrets management with HashiCorp Vault with elementary level demonstrations. The end result is a 360-degree education in Terraform - from installation to advanced usage across public clouds. Readers gain both theoretical foundations and hands-on skills transferred through practical examples. The book takes a holistic approach that equips practitioners to leverage Terraform in production environments.

[Note: Readers are advised not to misguide this book as a mastering book but does covers every possible task Terraform can perform.]

Key Learnings

  • Comprehensive introduction to Terraform's architecture, syntax, and semantics.
  • Practical examples to understand Infrastructure as Code (IaC) concept.
  • Detailed guidance on setting up Terraform in diverse environments.
  • Exploring Terraform's lifecycle, from initialization to resource destruction.
  • Insightful exploration of error management and troubleshooting techniques.
  • In-depth discussion on network management using Terraform for real-world scenarios.
  • Advanced topics coverage like Kubernetes resources and dynamic secrets.
  • Detailed explanation of Terraform's integration with CI/CD pipelines.
  • Extensive study of Terraform testing, including unit, integration, and compliance tests.
  • Knowledge on module creation and version control for efficient code reuse.

Table of Content

  1. Introduction to Terraform and Infrastructure as Code (IaC)
  2. Getting Started with Terraform
  3. Essentials of Terraform Configuration Language (HCL)
  4. Terraform Modules and Reusable Infrastructure
  5. Terraform State Management
  6. Provisioners and Provisioning Resources
  7. Working with Secrets
  8. Administering Networks with Terraform
  9. Advanced Topics in Terraform
  10. Administering Terraform As Expert

Audience

This book is designed for software professionals, system administrators, DevOps engineers, and cloud architects who aim to master Infrastructure as Code (IaC) using Terraform. Basic knowledge of cloud computing and some experience with coding will aid in comprehending the book's content.

Practical C++ Backend Programming

Crafting Databases, APIs, and Web Servers for High-Performance Backend
  • 100%

    Complete

  • PDF

  • EPUB

"Practical C++ Backend Programming" is a comprehensive walkthrough that provides readers with the necessary tools and knowledge to become proficient C++ backend developers. With a strong focus on real-world application and practical implementation, this book takes readers on a journey through the multifaceted landscape of backend development, making it an essential resource for any aspiring or current backend developer.

Starting with the basics, the book introduces C++, providing a solid foundation in the language, its structure, and core concepts with regards to backend programming. From there, readers dive into the more complex elements of backend development. Through our engaging sections, use-cases and sample examples, readers are introduced to advanced topics such as concurrent programming, exploring threading and multiprocessing to handle intensive computational tasks, thus laying the groundwork for scalable applications.

This book offers an in-depth look into APIs, specifically gRPC, along with caching strategies, and database management, using MongoDB as a NoSQL database of choice. All the while, readers will learn to implement these technologies in a practical context, building a blog application from scratch, thereby bridging the gap between theory and practical implementation. An entire section is dedicated to securing applications, wherein the book teaches authentication, authorization, and database security, and demonstrates how to implement these measures in the blog application.

Another utmost important part of this book is to cover testing strategies, teaching the reader how to employ Google Test (gtest) to create robust and fail-proof backend solutions. Finally, the journey culminates in a step-by-step walkthrough to deploying applications on AWS, ensuring the reader is equipped with the necessary skills to take their applications live.

Key Learnings

  • Detailed overview of C++ programming, catering to both beginners and experienced coders.
  • Practical exploration of concurrent programming for scalable and efficient application design.
  • Comprehensive understanding of API usage, specifically using the gRPC framework.
  • Deep dive into MongoDB for effective NoSQL database management and operations.
  • Thorough walkthrough to implementing caching strategies for performance optimization.
  • Strategic use of Nginx for handling web server needs including load balancing.
  • Hands-on guide to implementing security measures for databases, APIs, and web servers.
  • Instruction on employing Google Test for robust application testing and debugging.
  • Step-by-step guidance for deploying applications on AWS, ensuring real-world readiness.
  • Practical application of concepts via building and refining a blog application.

Table of Content

  1. Introduction to Backend Development
  2. C++ Refresher and Essentials
  3. Deep Dive into Algorithms
  4. Mastering Version Control - Git and GitHub
  5. Managing Database Operations with MongoDB
  6. Crafting Rest APIs with gRPC
  7. Dealing with Client-side and Server-side Caching
  8. Managing Web Servers with Nginx
  9. Testing Your C++ Backend
  10. Securing Your C++ Backend
  11. Deploying Your Application

Audience

This book is appropriate for readers with some background in C++ and nothing about back-end development. It's great for those just getting their feet wet in back-end development, as well as seasoned pros looking to hone their craft and learn something new. Whether you're a student, a professional, or a hobbyist, this book will teach you everything you need to know to master the art of C++ back-end development.

TensorFlow Developer Certification Guide

Crack Google’s official exam on getting skilled with managing production-grade ML models
  • 100%

    Complete

  • PDF

  • EPUB

Designed with both beginners and professionals in mind, the book is meticulously structured to cover a broad spectrum of concepts, applications, and hands-on practices that form the core of the TensorFlow Developer Certificate exam. Starting with foundational concepts, the book guides you through the fundamental aspects of TensorFlow, Machine Learning algorithms, and Deep Learning models.

The initial chapters focus on data preprocessing, exploratory analysis, and essential tools required for building robust models. The book then delves into Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and advanced neural network techniques such as GANs and Transformer Architecture. Emphasizing practical application, each chapter is peppered with detailed explanations, code snippets, and real-world examples, allowing you to apply the concepts in various domains such as text classification, sentiment analysis, object detection, and more.

A distinctive feature of the book is its focus on various optimization and regularization techniques that enhance model performance. As the book progresses, it navigates through the complexities of deploying TensorFlow models into production. It includes exhaustive sections on TensorFlow Serving, Kubernetes Cluster, and edge computing with TensorFlow Lite. The book provides practical insights into monitoring, updating, and handling possible errors in production, ensuring a smooth transition from development to deployment.

The final chapters are devoted to preparing you for the TensorFlow Developer Certificate exam. From strategies, tips, and coding challenges to a summary of the entire learning journey, these sections serve as a robust toolkit for exam readiness. With hints and solutions provided for challenges, you can assess your knowledge and fine-tune your problem solving skills. In essence, this book is more than a mere certification guide; it's a complete roadmap to mastering TensorFlow. It aligns perfectly with the objectives of the TensorFlow Developer Certificate exam, ensuring that you are not only well-versed in the theoretical aspects but are also skilled in practical applications.

Key Learnings

  • Comprehensive guide to TensorFlow, covering fundamentals to advanced topics, aiding seamless learning.
  • Alignment with TensorFlow Developer Certificate exam, providing targeted preparation and confidence.
  • In-depth exploration of neural networks, enhancing understanding of model architecture and function.
  • Hands-on examples throughout, ensuring practical understanding and immediate applicability of concepts.
  • Detailed insights into model optimization, including regularization, boosting model performance.
  • Extensive focus on deployment, from TensorFlow Serving to Kubernetes, for real-world applications.
  • Exploration of innovative technologies like BiLSTM, attention mechanisms, Transformers, fostering creativity.
  • Step-by-step coding challenges, enhancing problem-solving skills, mirroring real-world scenarios.
  • Coverage of potential errors in deployment, offering practical solutions, ensuring robust applications.
  • Continual emphasis on practical, applicable knowledge, making it suitable for all levels

Table of Contents

  1. Introduction to Machine Learning and TensorFlow 2.x
  2. Up and Running with Neural Networks
  3. Building Basic Machine Learning Models
  4. Image Recognition with CNN
  5. Object Detection Algorithms
  6. Text Recognition and Natural Language Processing
  7. Strategies to Prevent Overfitting & Underfitting
  8. Advanced Neural Networks for NLP
  9. Productionizing TensorFlow Models
  10. Preparing for TensorFlow Developer Certificate Exam

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