Recurrent Neural Networks with Python Quick Start Guide. Sequential learning and language modeling with TensorFlow

Simeon Kostadinov

Developers struggle to find an easy-to-follow learning resource for implementing Recurrent Neural Network (RNN) models. RNNs are the state-of-the-art model in deep learning for dealing with sequential data. From language translation to generating captions for an image, RNNs are used to continuously improve results. This book will teach you the fundamentals of RNNs, with example applications in Python and the TensorFlow library. The examples are accompanied by the right combination of theoretical knowledge and real-world implementations of concepts to build a solid foundation of neural network modeling.Your journey starts with the simplest RNN model, where you can grasp the fundamentals. The book then builds on this by proposing more advanced and complex algorithms. We use them to explain how a typical state-of-the-art RNN model works. From generating text to building a language translator, we show how some of today's most powerful AI applications work under the hood.After reading the book, you will be confident with the fundamentals of RNNs, and be ready to pursue further study, along with developing skills in this exciting field.

Learning Python Networking. A complete guide to build and deploy strong networking capabilities using Python 3.7 and Ansible - Second Edition

José Manuel Ortega, Dr. M. O. Faruque Sarker, Sam Washington

Network programming has always been a demanding task. With full-featured and well-documented libraries all the way up the stack, Python makes network programming the enjoyable experience it should be.Starting with a walk through of today's major networking protocols, through this book, you'll learn how to employ Python for network programming, how to request and retrieve web resources, and how to extract data in major formats over the web. You will utilize Python for emailing using different protocols, and you'll interact with remote systems and IP and DNS networking. You will cover the connection of networking devices and configuration using Python 3.7, along with cloud-based network management tasks using Python.As the book progresses, socket programming will be covered, followed by how to design servers, and the pros and cons of multithreaded and event-driven architectures. You'll develop practical clientside applications, including web API clients, email clients, SSH, and FTP. These applications will also be implemented through existing web application frameworks.

Mastering OpenStack. Design, deploy, and manage clouds in mid to large IT infrastructures - Second Edition

Omar Khedher, Chandan Dutta

In this second edition, you will get to grips with the latest features of OpenStack. Starting with an overview of the OpenStack architecture, you'll see how to adopt the DevOps style of automation while deploying and operating in an OpenStack environment. We'll show you how to create your own OpenStack private cloud. Then you'll learn about various hypervisors and container technology supported by OpenStack. You'll get an understanding about the segregation of compute nodes based on reliability and availability needs. We'll cover various storage types in OpenStack and advanced networking aspects such as SDN and NFV.Next, you'll understand the OpenStack infrastructure from a cloud user point of view. Moving on, you'll develop troubleshooting skills, and get a comprehensive understanding of services such as high availability and failover in OpenStack. Finally, you will gain experience of running a centralized logging server and monitoring OpenStack services. The book will show you how to carry out performance tuning based on OpenStack service logs. You will be able to master OpenStack benchmarking and performance tuning. By the end of the book, you'll be ready to take steps to deploy and manage an OpenStack cloud with the latest open source technologies.

Hands-On Deep Learning for Games. Leverage the power of neural networks and reinforcement learning to build intelligent games

Micheal Lanham

The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development. We will take a look at the foundations of multi-layer perceptron’s to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments.As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning.

Python High Performance. Build high-performing, concurrent, and distributed applications - Second Edition

Dr. Gabriele Lanaro

Python is a versatile language that has found applications in many industries. The clean syntax, rich standard library, and vast selection of third-party libraries make Python a wildly popular language. Python High Performance is a practical guide that shows how to leverage the power of both native and third-party Python libraries to build robust applications. The book explains how to use various profilers to find performance bottlenecks and apply the correct algorithm to fix them. The reader will learn how to effectively use NumPy and Cython to speed up numerical code. The book explains concepts of concurrent programming and how to implement robust and responsive applications using Reactive programming. Readers will learn how to write code for parallel architectures using Tensorflow and Theano, and use a cluster of computers for large-scale computations using technologies such as Dask and PySpark. By the end of the book, readers will have learned to achieve performance and scale from their Python applications.

Learning C for Arduino. A comprehensive guide that will help you ace C's fundamentals using the powerful Arduino board

Syed Omar Faruk Towaha

This book will start with the fundamentals of C programming and programming topics, such data types, functions, decision making, program loops, pointers, and structures, with the help of an Arduino board. Then you will get acquainted with Arduino interactions with sensors, LEDs, and autonomous systems and setting up the Arduino environment. Moving on you will also learn how to work on the digital and analog I/O, establish serial communications with autonomous systems, and integrate with electronic devices. By the end of the book, you will be able to make basic projects such as LED cube and smart weather system that leverages C.

Machine Learning with TensorFlow 1.x. Second generation machine learning with Google's brainchild - TensorFlow 1.x

Saif Ahmed, Quan Hua, Shams Ul Azeem

Google's TensorFlow is a game changer in the world of machine learning. It has made machine learning faster, simpler, and more accessible than ever before. This book will teach you how to easily get started with machine learning using the power of Python and TensorFlow 1.x. Firstly, you’ll cover the basic installation procedure and explore the capabilities of TensorFlow 1.x. This is followed by training and running the first classifier, and coverage of the unique features of the library including data ?ow graphs, training, and the visualization of performance with TensorBoard—all within an example-rich context using problems from multiple industries. You’ll be able to further explore text and image analysis, and be introduced to CNN models and their setup in TensorFlow 1.x. Next, you’ll implement a complete real-life production system from training to serving a deep learning model. As you advance you’ll learn about Amazon Web Services (AWS) and create a deep neural network to solve a video action recognition problem. Lastly, you’ll convert the Caffe model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-Slim.By the end of this book, you will be geared up to take on any challenges of implementing TensorFlow 1.x in your machine learning environment.

Jupyter for Data Science. Exploratory analysis, statistical modeling, machine learning, and data visualization with Jupyter

Dan Toomey

Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create documents that contain live code, equations, and visualizations. This book is a comprehensive guide to getting started with data science using the popular Jupyter notebook. If you are familiar with Jupyter notebook and want to learn how to use its capabilities to perform various data science tasks, this is the book for you! From data exploration to visualization, this book will take you through every step of the way in implementing an effective data science pipeline using Jupyter. You will also see how you can utilize Jupyter's features to share your documents and codes with your colleagues. The book also explains how Python 3, R, and Julia can be integrated with Jupyter for various data science tasks.By the end of this book, you will comfortably leverage the power of Jupyter to perform various tasks in data science successfully.

Enterprise Application Architecture with .NET Core. An architectural journey into the Microsoft .NET open source platform

Ganesan Senthilvel, Ovais Mehboob Ahmed Khan, Habib Qureshi

If you want to design and develop enterprise applications using .NET Core as the development framework and learn about industry-wide best practices and guidelines, then this book is for you. The book starts with a brief introduction to enterprise architecture, which will help you to understand what enterprise architecture is and what the key components are. It will then teach you about the types of patterns and the principles of software development, and explain the various aspects of distributed computing to keep your applications effective and scalable. These chapters act as a catalyst to start the practical implementation, and design and develop applications using different architectural approaches, such as layered architecture, service oriented architecture, microservices and cloud-specific solutions. Gradually, you will learn about the different approaches and models of the Security framework and explore various authentication models and authorization techniques, such as social media-based authentication and safe storage using app secrets. By the end of the book, you will get to know the concepts and usage of the emerging fields, such as DevOps, BigData, architectural practices, and Artificial Intelligence.

Go Systems Programming. Master Linux and Unix system level programming with Go

Mihalis Tsoukalos

Go is the new systems programming language for Linux and Unix systems. It is also the language in which some of the most prominent cloud-level systems have been written, such as Docker. Where C programmers used to rule, Go programmers are in demand to write highly optimized systems programming code.Created by some of the original designers of C and Unix, Go expands the systems programmers toolkit and adds a mature, clear programming language. Traditional system applications become easier to write since pointers are not relevant and garbage collection has taken away the most problematic area for low-level systems code: memory management.This book opens up the world of high-performance Unix system applications to the beginning Go programmer. It does not get stuck on single systems or even system types, but tries to expand the original teachings from Unix system level programming to all types of servers, the cloud, and the web.

Python Network Programming Cookbook. Practical solutions to overcome real-world networking challenges - Second Edition

Pradeeban Kathiravelu, Gary Berger, Dr. M. O. Faruque Sarker

Python Network Programming Cookbook - Second Edition highlights the major aspects of network programming in Python, starting from writing simple networking clients to developing and deploying complex Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) systems. It creates the building blocks for many practical web and networking applications that rely on various networking protocols. It presents the power and beauty of Python to solve numerous real-world tasks in the area of network programming, network and system administration, network monitoring, and web-application development.In this edition, you will also be introduced to network modelling to build your own cloud network. You will learn about the concepts and fundamentals of SDN and then extend your network with Mininet. Next, you’ll find recipes on Authentication, Authorization, and Accounting (AAA) and open and proprietary SDN approaches and frameworks. You will also learn to configure the Linux Foundation networking ecosystem and deploy and automate your networks with Python in the cloud and the Internet scale.By the end of this book, you will be able to analyze your network security vulnerabilities using advanced network packet capture and analysis techniques.

Intelligent Projects Using Python. 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras

Santanu Pattanayak

This book will be a perfect companion if you want to build insightful projects from leading AI domains using Python.The book covers detailed implementation of projects from all the core disciplines of AI. We start by covering the basics of how to create smart systems using machine learning and deep learning techniques. You will assimilate various neural network architectures such as CNN, RNN, LSTM, to solve critical new world challenges. You will learn to train a model to detect diabetic retinopathy conditions in the human eye and create an intelligent system for performing a video-to-text translation. You will use the transfer learning technique in the healthcare domain and implement style transfer using GANs. Later you will learn to build AI-based recommendation systems, a mobile app for sentiment analysis and a powerful chatbot for carrying customer services. You will implement AI techniques in the cybersecurity domain to generate Captchas. Later you will train and build autonomous vehicles to self-drive using reinforcement learning. You will be using libraries from the Python ecosystem such as TensorFlow, Keras and more to bring the core aspects of machine learning, deep learning, and AI.By the end of this book, you will be skilled to build your own smart models for tackling any kind of AI problems without any hassle.

Real-World SRE. The Survival Guide for Responding to a System Outage and Maximizing Uptime

Nat Welch

Real-World SRE is the go-to survival guide for the software developer in the middle of catastrophic website failure. Site Reliability Engineering (SRE) has emerged on the frontline as businesses strive to maximize uptime. This book is a step-by-step framework to follow when your website is down and the countdown is on to fix it. Nat Welch has battle-hardened experience in reliability engineering at some of the biggest outage-sensitive companies on the internet. Arm yourself with his tried-and-tested methods for monitoring modern web services, setting up alerts, and evaluating your incident response.Real-World SRE goes beyond just reacting to disaster—uncover the tools and strategies needed to safely test and release software, plan for long-term growth, and foresee future bottlenecks. Real-World SRE gives you the capability to set up your own robust plan of action to see you through a company-wide website crisis.The final chapter of Real-World SRE is dedicated to acing SRE interviews, either in getting a first job or a valued promotion.

Mastering Kibana 6.x. Visualize your Elastic Stack data with histograms, maps, charts, and graphs

Anurag Srivastava

Kibana is one of the popular tools among data enthusiasts for slicing and dicing large datasets and uncovering Business Intelligence (BI) with the help of its rich and powerful visualizations.To begin with, Mastering Kibana 6.x quickly introduces you to the features of Kibana 6.x, before teaching you how to create smart dashboards in no time. You will explore metric analytics and graph exploration, followed by understanding how to quickly customize Kibana dashboards. In addition to this, you will learn advanced analytics such as maps, hits, and list analytics. All this will help you enhance your skills in running and comparing multiple queries and filters, influencing your data visualization skills at scale.With Kibana’s Timelion feature, you can analyze time series data with histograms and stats analytics. By the end of this book, you will have created a speedy machine learning job using X-Pack capabilities.

Java EE 8 High Performance. Master techniques such as memory optimization, caching, concurrency, and multithreading to achieve maximum performance from your enterprise applications

Romain Manni-Bucau

The ease with which we write applications has been increasing, but with this comes the need to address their performance. A balancing act between easily implementing complex applications and keeping their performance optimal is a present-day need. In this book, we explore how to achieve this crucial balance while developing and deploying applications with Java EE 8. The book starts by analyzing various Java EE specifications to identify those potentially affecting performance adversely. Then, we move on to monitoring techniques that enable us to identify performance bottlenecks and optimize performance metrics. Next, we look at techniques that help us achieve high performance: memory optimization, concurrency, multi-threading, scaling, and caching. We also look at fault tolerance solutions and the importance of logging. Lastly, you will learn to benchmark your application and also implement solutions for continuous performance evaluation.By the end of the book, you will have gained insights into various techniques and solutions that will help create high-performance applications in the Java EE 8 environment.

Building Serverless Applications with Python. Develop fast, scalable, and cost-effective web applications that are always available

Jalem Raj Rohit

Serverless architectures allow you to build and run applications and services without having to manage the infrastructure. Many companies have adopted this architecture to save cost and improve scalability. This book will help you design serverless architectures for your applications with AWS and Python.The book is divided into three modules. The first module explains the fundamentals of serverless architecture and how AWS lambda functions work. In the next module, you will learn to build, release, and deploy your application to production. You will also learn to log and test your application. In the third module, we will take you through advanced topics such as building a serverless API for your application. You will also learn to troubleshoot and monitor your app and master AWS lambda programming concepts with API references. Moving on, you will also learn how to scale up serverless applications and handle distributed serverless systems in production.By the end of the book, you will be equipped with the knowledge required to build scalable and cost-efficient Python applications with a serverless framework.