Explore C# and the .NET Core framework to create applications and optimize them with ASP.NET Core 2 Key Features *Get to grips with multi-threaded, concurrent, and asynchronous programming in C# and .NET Core *Develop modern, cross-platform applications with .NET Core 2.0 and C# 7.0 *Create efficient web applications with ASP.NET Core 2. Book Description C# is a widely used programming language, thanks to its easy learning curve, versatility, and support for modern paradigms. The language is used to create desktop apps, background services, web apps, and mobile apps. .NET Core is open source and compatible with Mac OS and Linux. There is no limit to what you can achieve with C# and .NET Core. This Learning Path begins with the basics of C# and object-oriented programming (OOP) and explores features of C#, such as tuples, pattern matching, and out variables. You will understand.NET Standard 2.0 class libraries and ASP.NET Core 2.0, and create professional websites, services, and applications. You will become familiar with mobile app development using Xamarin.Forms and learn to develop high-performing applications by writing optimized code with various profiling techniques. By the end of C# 7 and .NET: Designing Modern Cross-platform Applications, you will have all the knowledge required to build modern, cross-platform apps using C# and .NET. This Learning Path includes content from the following Packt products: *C# 7.1 and .NET Core 2.0 - Modern Cross-Platform Development - Third Edition by Mark J. Price *C# 7 and .NET Core 2.0 High Performance by Ovais Mehboob Ahmed Khan What you will learn *Explore ASP.NET Core to create professional web applications *Master OOP with C# to increase code reusability and efficiency *Protect your data using encryption and hashing *Measure application performance using BenchmarkDotNet *Use design techniques to increase your application’s performance *Learn memory management techniques in .NET Core *Understand tools and techniques to monitor application performance Who this book is for This Learning Path is designed for developers who want to gain a solid foundation in C# and .NET Core, and want to build cross-platform applications. To gain maximum benefit from this Learning Path, you must have basic knowledge of C#.

Explore and understand data with the powerful data visualization techniques of Tableau, and then communicate insights in powerful ways Key Features *Apply best practices in data visualization and chart types exploration *Explore the latest version of Tableau Desktop with hands-on examples *Understand the fundamentals of Tableau storytelling Book Description Graphical presentation of data enables us to easily understand complex data sets. Tableau 10 Complete Reference provides easy-to-follow recipes with several use cases and real-world business scenarios to get you up and running with Tableau 10. This Learning Path begins with the history of data visualization and its importance in today's businesses. You'll also be introduced to Tableau - how to connect, clean, and analyze data in this visual analytics software. Then, you'll learn how to apply what you've learned by creating some simple calculations in Tableau and using Table Calculations to help drive greater analysis from your data. Next, you'll explore different advanced chart types in Tableau. These chart types require you to have some understanding of the Tableau interface and understand basic calculations. You’ll study in detail all dashboard techniques and best practices. A number of recipes specifically for geospatial visualization, analytics, and data preparation are also covered. Last but not least, you'll learn about the power of storytelling through the creation of interactive dashboards in Tableau. Through this Learning Path, you will gain confidence and competence to analyze and communicate data and insights more efficiently and effectively by creating compelling interactive charts, dashboards, and stories in Tableau. This Learning Path includes content from the following Packt products: *Learning Tableau 10 - Second Edition by N. Milligan *Getting Started with Tableau 2018.x by Tristan Guillevin What you will learn *Build effective visualizations, dashboards, and story points *Build basic to more advanced charts with step-by-step recipes *Become familiar row-level, aggregate, and table calculations *Dig deep into data with clustering and distribution models *Prepare and transform data for analysis *Leverage Tableau’s mapping capabilities to visualize data *Use data storytelling techniques to aid decision making strategy Who this book is for Tableau 10 Complete Reference is designed for anyone who wants to understand their data better and represent it in an effective manner. It is also used for BI professionals and data analysts who want to do better at their jobs.

Build powerful data analytics applications with this business intelligence tool and overcome all your business challenges Key Features *Master time-saving techniques and make your QlikView development more efficient *Perform geographical analysis and sentiment analysis in your QlikView applications *Explore advanced QlikView techniques, tips, and tricks to deliver complex business requirements Book Description QlikView is one of the most flexible and powerful business intelligence platforms around, and if you want to transform data into insights, it is one of the best options you have at hand. Use this Learning Path, to explore the many features of QlikView to realize the potential of your data and present it as impactful and engaging visualizations. Each chapter in this Learning Path starts with an understanding of a business requirement and its associated data model and then helps you create insightful analysis and data visualizations around it. You will look at problems that you might encounter while visualizing complex data insights using QlikView, and learn how to troubleshoot these and other not-so-common errors. This Learning Path contains real-world examples from a variety of business domains, such as sales, finance, marketing, and human resources. With all the knowledge that you gain from this Learning Path, you will have all the experience you need to implement your next QlikView project like a pro. This Learning Path includes content from the following Packt products: *QlikView for Developers by Miguel ?ngel García, Barry Harmsen *Mastering QlikView by Stephen Redmond *Mastering QlikView Data Visualization by Karl Pover What you will learn *Deliver common business requirements using advanced techniques *Load data from disparate sources to build associative data models *Understand when to apply more advanced data visualization *Utilize the built-in aggregation functions for complex calculations *Build a data architecture that supports scalable QlikView deployments *Troubleshoot common data visualization errors in QlikView *Protect your QlikView applications and data Who this book is for This Learning Path is designed for developers who want to go beyond their technical knowledge of QlikView and understand how to create analysis and data visualizations that solve real business needs. To grasp the concepts explained in this Learning Path, you should have a basic understanding of the common QlikView functions and some hands-on experience with the tool.

Step-by-step guide to build high performing predictive applications Key Features *Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects *Explore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanations *Learn to deploy a predictive model's results as an interactive application Book Description Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming. What you will learn *Get to grips with the main concepts and principles of predictive analytics *Learn about the stages involved in producing complete predictive analytics solutions *Understand how to define a problem, propose a solution, and prepare a dataset *Use visualizations to explore relationships and gain insights into the dataset *Learn to build regression and classification models using scikit-learn *Use Keras to build powerful neural network models that produce accurate predictions *Learn to serve a model's predictions as a web application Who this book is for This book is for data analysts, data scientists, data engineers, and Python developers who want to learn about predictive modeling and would like to implement predictive analytics solutions using Python's data stack. People from other backgrounds who would like to enter this exciting field will greatly benefit from reading this book. All you need is to be proficient in Python programming and have a basic understanding of statistics and college-level algebra.

A comprehensive guide to Android forensics, from setting up the workstation to analyzing key artifacts Key Features *Get up and running with modern mobile forensic strategies and techniques *Analyze the most popular Android applications using free and open source forensic tools *Learn malware detection and analysis techniques to investigate mobile cybersecurity incidents Book Description Many forensic examiners rely on commercial, push-button tools to retrieve and analyze data, even though there is no tool that does either of these jobs perfectly. Learning Android Forensics will introduce you to the most up-to-date Android platform and its architecture, and provide a high-level overview of what Android forensics entails. You will understand how data is stored on Android devices and how to set up a digital forensic examination environment. As you make your way through the chapters, you will work through various physical and logical techniques to extract data from devices in order to obtain forensic evidence. You will also learn how to recover deleted data and forensically analyze application data with the help of various open source and commercial tools. In the concluding chapters, you will explore malware analysis so that you’ll be able to investigate cybersecurity incidents involving Android malware. By the end of this book, you will have a complete understanding of the Android forensic process, you will have explored open source and commercial forensic tools, and will have basic skills of Android malware identification and analysis. What you will learn *Understand Android OS and architecture *Set up a forensics environment for Android analysis *Perform logical and physical data extractions *Learn to recover deleted data *Explore how to analyze application data *Identify malware on Android devices *Analyze Android malware Who this book is for If you are a forensic analyst or an information security professional wanting to develop your knowledge of Android forensics, then this is the book for you. Some basic knowledge of the Android mobile platform is expected.

Demonstrate fundamentals of Deep Learning and neural network methodologies using Keras 2.x Key Features *Experimental projects showcasing the implementation of high-performance deep learning models with Keras. * *Use-cases across reinforcement learning, natural language processing, GANs and computer vision. * *Build strong fundamentals of Keras in the area of deep learning and artificial intelligence. Book Description Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more. By the end of this book, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems. What you will learn *Apply regression methods to your data and understand how the regression algorithm works *Understand the basic concepts of classification methods and how to implement them in the Keras environment *Import and organize data for neural network classification analysis *Learn about the role of rectified linear units in the Keras network architecture *Implement a recurrent neural network to classify the sentiment of sentences from movie reviews *Set the embedding layer and the tensor sizes of a network Who this book is for If you are a data scientist, machine learning engineer, deep learning practitioner or an AI engineer who wants to build speedy intelligent applications with minimal lines of codes, then this book is the best fit for you. Sound knowledge of machine learning and basic familiarity with Keras library would be useful.

Leverage the power of machine learning on mobiles and build intelligent mobile applications with ease Key Features *Build smart mobile applications for Android and iOS devices *Use popular machine learning toolkits such as Core ML and TensorFlow Lite *Explore cloud services for machine learning that can be used in mobile apps Book Description Machine learning presents an entirely unique opportunity in software development. It allows smartphones to produce an enormous amount of useful data that can be mined, analyzed, and used to make predictions. This book will help you master machine learning for mobile devices with easy-to-follow, practical examples. You will begin with an introduction to machine learning on mobiles and grasp the fundamentals so you become well-acquainted with the subject. You will master supervised and unsupervised learning algorithms, and then learn how to build a machine learning model using mobile-based libraries such as Core ML, TensorFlow Lite, ML Kit, and Fritz on Android and iOS platforms. In doing so, you will also tackle some common and not-so-common machine learning problems with regard to Computer Vision and other real-world domains. By the end of this book, you will have explored machine learning in depth and implemented on-device machine learning with ease, thereby gaining a thorough understanding of how to run, create, and build real-time machine-learning applications on your mobile devices. What you will learn *Build intelligent machine learning models that run on Android and iOS *Use machine learning toolkits such as Core ML, TensorFlow Lite, and more *Learn how to use Google Mobile Vision in your mobile apps *Build a spam message detection system using Linear SVM *Using Core ML to implement a regression model for iOS devices *Build image classification systems using TensorFlow Lite and Core ML Who this book is for If you are a mobile app developer or a machine learning enthusiast keen to use machine learning to build smart mobile applications, this book is for you. Some experience with mobile application development is all you need to get started with this book. Prior experience with machine learning will be an added bonus

Learn the foundations of blockchain technology - its core concepts and algorithmic solutions across cryptography, peer-to-peer technology, and game theory. Key Features * Learn the core concepts and foundations of the blockchain and cryptocurrencies * Understand the protocols and algorithms behind decentralized applications * Master how to architect, build, and optimize blockchain applications Book Description Blockchain technology is a combination of three popular concepts: cryptography, peer-to-peer networking, and game theory. This book is for anyone who wants to dive into blockchain from first principles and learn how decentralized applications and cryptocurrencies really work. This book begins with an overview of blockchain technology, including key definitions, its purposes and characteristics, so you can assess the full potential of blockchain. All essential aspects of cryptography are then presented, as the backbone of blockchain. For readers who want to study the underlying algorithms of blockchain, you’ll see Python implementations throughout. You’ll then learn how blockchain architecture can create decentralized applications. You’ll see how blockchain achieves decentralization through peer-to-peer networking, and how a simple blockchain can be built in a P2P network. You’ll learn how these elements can implement a cryptocurrency such as Bitcoin, and the wider applications of blockchain work through smart contracts. Blockchain optimization techniques, and blockchain security strategies are then presented. To complete this foundation, we consider blockchain applications in the financial and non-financial sectors, and also analyze the future of blockchain. A study of blockchain use cases includes supply chains, payment systems, crowdfunding, and DAOs, which rounds out your foundation in blockchain technology. What you will learn * The core concepts and technical foundations of blockchain * The algorithmic principles and solutions that make up blockchain and cryptocurrencies * Blockchain cryptography explained in detail * How to realize blockchain projects with hands-on Python code * How to architect the blockchain and blockchain applications * Decentralized application development with MultiChain, NEO, and Ethereum * Optimizing and enhancing blockchain performance and security * Classical blockchain use cases and how to implement them Who this book is for This book is for anyone who wants to dive into blockchain technology from first principles and build a foundational knowledge of blockchain. Familiarity with Python will be helpful if you want to follow how the blockchain protocols are implemented. For readers who are blockchain application developers, most of the applications used in this book can be executed on any platform.

An effective guide to becoming an AWS Certified SysOps Administrator Key Features * Not only pass the certification with confidence but also enhance your skills to solving real-world scenarios. * A practical guide to getting you hands-on experience with application management, deployment, operation. * Enhance your AWS skills with practice questions and mock tests. Book Description AWS certifications are becoming one of the must have certifications for any IT professional working on an AWS Cloud platform. This book will act as your one stop preparation guide to validate your technical expertise in deployment, management, and operations on the AWS platform. Along with exam specific content this book will also deep dive into real world scenarios and hands-on instructions. This book will revolve around concepts like teaching you to deploy, manage, and operate scalable, highly available, and fault tolerant systems on AWS. You will also learn to migrate an existing on-premises application to AWS. You get hands-on experience in selecting the appropriate AWS service based on compute, data, or security requirements. This book will also get you well versed with estimating AWS usage costs and identifying operational cost control mechanisms. By the end of this book, you will be all prepared to implement and manage resources efficiently on the AWS cloud along with confidently passing the AWS Certified SysOps Administrator – Associate exam. What you will learn * Create and manage users, groups, and permissions using AWS IAM services * Create a secure VPC with public and private subnets, Network Access Control, and security groups * Get started with launching your first EC2 instance, and working with it * Handle application traffic with ELB and monitor AWS resources with CloudWatch * Work with S3, Glacier, and CloudFront * Work across distributed application components using SWF * Understand event-based processing with Lambda and messaging SQS and SNS in AWS * Get familiar with AWS deployment concepts and tools including Elastic Beanstalk, CloudFormation and AWS OpsWorks Who this book is for If you are a system administrator or a system engineer interested in leveraging the AWS platform to deploy applications then, this book is for you. IT professionals interested in passing the AWS Certified Sysops Administrator will also benefit from this book. Some basic understanding of working AWS components would do wonders.

Explore the best tools and techniques to create lightweight, maintainable, and scalable Python web services Key Features *Combine Python with different data sources to build complex RESTful APIs from scratch *Configure and fine-tune your APIs using the best tools and techniques available *Use command-line and GUI tools to test CRUD operations performed by RESTful Web Services or APIs Book Description Python is the language of choice for millions of developers worldwide that builds great web services in RESTful architecture. This second edition of Hands-On RESTful Python Web Services will cover the best tools you can use to build engaging web services. This book shows you how to develop RESTful APIs using the most popular Python frameworks and all the necessary stacks with Python, combined with related libraries and tools. You’ll learn to incorporate all new features of Python 3.7, Flask 1.0.2, Django 2.1, Tornado 5.1, and also a new framework, Pyramid. As you advance through the chapters, you will get to grips with each of these frameworks to build various web services, and be shown use cases and best practices covering when to use a particular framework. You’ll then successfully develop RESTful APIs with all frameworks and understand how each framework processes HTTP requests and routes URLs. You’ll also discover best practices for validation, serialization, and deserialization. In the concluding chapters, you will take advantage of specific features available in certain frameworks such as integrated ORMs, built-in authorization and authentication, and work with asynchronous code. At the end of each framework, you will write tests for RESTful APIs and improve code coverage. By the end of the book, you will have gained a deep understanding of the stacks needed to build RESTful web services. What you will learn *Select the most appropriate framework based on requirements *Develop complex RESTful APIs from scratch using Python *Use requests handlers, URL patterns, serialization, and validations *Add authentication, authorization, and interaction with ORMs and databases *Debug, test, and improve RESTful APIs with four frameworks *Design RESTful APIs with frameworks and create automated tests Who this book is for This book is for web developers who have a working knowledge of Python and would like to build amazing web services by taking advantage of the various frameworks of Python. You should have some knowledge of RESTful APIs.

Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems Key Features *Master supervised, unsupervised, and semi-supervised ML algorithms and their implementation *Build deep learning models for object detection, image classification, similarity learning, and more *Build, deploy, and scale end-to-end deep neural network models in a production environment Book Description This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: *Mastering Machine Learning Algorithms by Giuseppe Bonaccorso *Mastering TensorFlow 1.x by Armando Fandango *Deep Learning for Computer Vision by Rajalingappaa Shanmugamani What you will learn *Explore how an ML model can be trained, optimized, and evaluated *Work with Autoencoders and Generative Adversarial Networks *Explore the most important Reinforcement Learning techniques *Build end-to-end deep learning (CNN, RNN, and Autoencoders) models Who this book is for This Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this Learning Path.

Design, develop, and master efficient Power BI solutions for impactful business insights Key Features *Get to grips with the fundamentals of Microsoft Power BI *Combine data from multiple sources, create visuals, and publish reports across platforms *Understand Power BI concepts with real-world use cases Book Description Microsoft Power BI Complete Reference Guide gets you started with business intelligence by showing you how to install the Power BI toolset, design effective data models, and build basic dashboards and visualizations that make your data come to life. In this Learning Path, you will learn to create powerful interactive reports by visualizing your data and learn visualization styles, tips and tricks to bring your data to life. You will be able to administer your organization's Power BI environment to create and share dashboards. You will also be able to streamline deployment by implementing security and regular data refreshes. Next, you will delve deeper into the nuances of Power BI and handling projects. You will get acquainted with planning a Power BI project, development, and distribution of content, and deployment. You will learn to connect and extract data from various sources to create robust datasets, reports, and dashboards. Additionally, you will learn how to format reports and apply custom visuals, animation and analytics to further refine your data. By the end of this Learning Path, you will learn to implement the various Power BI tools such as on-premises gateway together along with staging and securely distributing content via apps. This Learning Path includes content from the following Packt products: *Microsoft Power BI Quick Start Guide by Devin Knight et al. *Mastering Microsoft Power BI by Brett Powell What you will learn *Connect to data sources using both import and DirectQuery options *Leverage built-in and custom visuals to design effective reports *Administer a Power BI cloud tenant for your organization *Deploy your Power BI Desktop files into the Power BI Report Server *Build efficient data retrieval and transformation processes Who this book is for Microsoft Power BI Complete Reference Guide is for those who want to learn and use the Power BI features to extract maximum information and make intelligent decisions that boost their business. If you have a basic understanding of BI concepts and want to learn how to apply them using Microsoft Power BI, then Learning Path is for you. It consists of real-world examples on Power BI and goes deep into the technical issues, covers additional protocols, and much more.

Work on practical computer vision projects covering advanced object detector techniques and modern deep learning and machine learning algorithms Key Features *Learn about the new features that help unlock the full potential of OpenCV 4 *Build face detection applications with a cascade classifier using face landmarks *Create an optical character recognition (OCR) model using deep learning and convolutional neural networks Book Description Mastering OpenCV, now in its third edition, targets computer vision engineers taking their first steps toward mastering OpenCV. Keeping the mathematical formulations to a solid but bare minimum, the book delivers complete projects from ideation to running code, targeting current hot topics in computer vision such as face recognition, landmark detection and pose estimation, and number recognition with deep convolutional networks. You’ll learn from experienced OpenCV experts how to implement computer vision products and projects both in academia and industry in a comfortable package. You’ll get acquainted with API functionality and gain insights into design choices in a complete computer vision project. You’ll also go beyond the basics of computer vision to implement solutions for complex image processing projects. By the end of the book, you will have created various working prototypes with the help of projects in the book and be well versed with the new features of OpenCV4. What you will learn *Build real-world computer vision problems with working OpenCV code samples *Uncover best practices in engineering and maintaining OpenCV projects *Explore algorithmic design approaches for complex computer vision tasks *Work with OpenCV’s most updated API (v4.0.0) through projects *Understand 3D scene reconstruction and Structure from Motion (SfM) *Study camera calibration and overlay AR using the ArUco Module Who this book is for This book is for those who have a basic knowledge of OpenCV and are competent C++ programmers. You need to have an understanding of some of the more theoretical/mathematical concepts, as we move quite quickly throughout the book.

Develop technical skills and expertise to automate AWS networking tasks Key Features * A fast paced guide that will help you pass the exam with confidence * Learn advanced skill sets to build effective AWS networking solutions * Enhance your AWS skills with practice exercises and mock tests Book Description Amazon has recently come up a with specialty certifications which validates a particular user's expertise that he/she would want to build a career in. Since the Cloud market now demands of AWS networking skills this becomes the most wanted certification to upheld ones industry portfolio. This book would be your ideal companion to getting skilled with complex and creative networking solutions. Cloud practitioners or associate-level certified individuals interested in validating advanced skills in networking can opt for this practical guide. This book will include topics that will help you design and implement AWS and hybrid IT network architectures along with some network automation tasks. You will also delve deep into topics that will help you design and maintain network architecture for all AWS services. Like most of our certification guides this book will also follow a unique approach of testing your learning with chapter-level practice exercises and certification-based mock tests. The exam mock tests will help you gauge whether you are ready to take the certification exam or not. This book will also be an advanced guide for networking professionals to enhance their networking skills and get certified. By the end of this book, you will be all equipped with AWS networking concepts and techniques and will have mastered core architectural best practices. What you will learn * Formulate solution plans and provide guidance on AWS architecture best practices * Design and deploy scalable, highly available, and fault-tolerant systems on AWS * Identify the tools required to replicate an on-premises network in AWS * Analyze the access and egress of data to and from AWS * Select the appropriate AWS service based on data, compute, database, or security requirements * Estimate AWS costs and identify cost control mechanisms Who this book is for If you are a system administrator, or a network engineer interested in getting certified with an advanced Cloud networking certification then this book is for you. Prior experience in Cloud administration and networking would be necessary.

Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and Keras Key Features * Explore neural network architecture and understand how it functions * Learn algorithms to solve common problems using back propagation and perceptrons * Understand how to apply neural networks to applications with the help of useful illustrations Book Description Neural networks play a very important role in deep learning and artificial intelligence (AI), with applications in a wide variety of domains, right from medical diagnosis, to financial forecasting, and even machine diagnostics. Hands-On Neural Networks is designed to guide you through learning about neural networks in a practical way. The book will get you started by giving you a brief introduction to perceptron networks. You will then gain insights into machine learning and also understand what the future of AI could look like. Next, you will study how embeddings can be used to process textual data and the role of long short-term memory networks (LSTMs) in helping you solve common natural language processing (NLP) problems. The later chapters will demonstrate how you can implement advanced concepts including transfer learning, generative adversarial networks (GANs), autoencoders, and reinforcement learning. Finally, you can look forward to further content on the latest advancements in the field of neural networks. By the end of this book, you will have the skills you need to build, train, and optimize your own neural network model that can be used to provide predictable solutions. What you will learn * Learn how to train a network by using backpropagation * Discover how to load and transform images for use in neural networks * Study how neural networks can be applied to a varied set of applications * Solve common challenges faced in neural network development * Understand the transfer learning concept to solve tasks using Keras and Visual Geometry Group (VGG) network * Get up to speed with advanced and complex deep learning concepts like LSTMs and NLP * Explore innovative algorithms like GANs and deep reinforcement learning Who this book is for If you are interested in artificial intelligence and deep learning and want to further your skills, then this intermediate-level book is for you. Some knowledge of statistics will help you get the most out of this book.

A practical guide to building high performance systems for object detection, segmentation, video processing, smartphone applications, and more. This book is based on the alpha version of TensorFlow 2. Key Features * Discover how to build, train, and serve your own deep neural networks with TensorFlow 2 and Keras * Apply modern solutions to a wide range of applications such as object detection and video analysis * Learn how to run your models on mobile devices and webpages and improve their performance Book Description Computer vision solutions are becoming increasingly common, making their way in fields such as health, automobile, social media, and robotics. This book will help you explore TensorFlow 2, the brand new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. Hands-On Computer Vision with TensorFlow 2 starts with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface, and move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the book demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) to create and edit images, and LSTMs to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts. By the end of the book, you will have both the theoretical understanding and practical skills to solve advanced computer vision problems with TensorFlow 2.0. What you will learn * Create your own neural networks from scratch * Classify images with modern architectures including Inception and ResNet * Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net * Tackle problems in developing self-driving cars and facial emotion recognition systems * Boost your application’s performance with transfer learning, GANs, and domain adaptation * Use recurrent neural networks for video analysis * Optimize and deploy your networks on mobile devices and in the browser Who this book is for If you’re new to deep learning and have some background in Python programming and image processing, like reading/writing image files and editing pixels, this book is for you. Even if you’re an expert curious about the new TensorFlow 2 features, you’ll find this book useful. While some theoretical explanations require knowledge in algebra and calculus, the book covers concrete examples for learners focused on practical applications such as visual recognition for self-driving cars and smartphone apps.

Build and train scalable neural network models on various platforms by leveraging the power of Caffe2 Key Features * Migrate models trained with other deep learning frameworks on Caffe2 * Integrate Caffe2 with Android or iOS and implement deep learning models for mobile devices * Leverage the distributed capabilities of Caffe2 to build models that scale easily Book Description Caffe2 is a popular deep learning library used for fast and scalable training and inference of deep learning models on various platforms. This book introduces you to the Caffe2 framework and shows how you can leverage its power to build, train, and deploy efficient neural network models at scale. It will cover the topics of installing Caffe2, composing networks using its operators, training models, and deploying models to different architectures. It will also show how to import models from Caffe and from other frameworks using the ONNX interchange format. It covers the topic of deep learning accelerators such as CPU and GPU and shows how to deploy Caffe2 models for inference on accelerators using inference engines. Caffe2 is built for deployment to a diverse set of hardware, using containers on the cloud and resource constrained hardware such as Raspberry Pi, which will be demonstrated. By the end of this book, you will be able to not only compose and train popular neural network models with Caffe2, but also be able to deploy them on accelerators, to the cloud and on resource constrained platforms such as mobile and embedded hardware. What you will learn * Build and install Caffe2 * Compose neural networks * Train neural network on CPU or GPU * Import a neural network from Caffe * Import deep learning models from other frameworks * Deploy models on CPU or GPU accelerators using inference engines * Deploy models at the edge and in the cloud Who this book is for Data scientists and machine learning engineers who wish to create fast and scalable deep learning models in Caffe2 will find this book to be very useful. Some understanding of the basic machine learning concepts and prior exposure to programming languages like C++ and Python will be useful.

Learn end-to-end automation testing techniques for web and mobile browsers using Selenium WebDriver, AppiumDriver, Java, and TestNG Key Features * Explore the Selenium grid architecture and build your own grid for browser and mobile devices * Use ExtentReports for processing results and SauceLabs for cloud-based test services * Unlock the full potential of Selenium to test your web applications. Book Description Selenium WebDriver 3.x is an open source API for testing both browser and mobile applications. With the help of this book, you can build a solid foundation and can easily perform end-to-end testing on web and mobile browsers.You'll begin by being introduced to the Selenium Page Object Model for software development. You'll architect your own framework with a scalable driver class, Java utility classes, and support for third-party tools and plugins. You'll design and build a Selenium grid from scratch to enable the framework to scale and support different browsers, mobile devices, and platforms.You'll strategize and handle a rich web UI using the advanced WebDriver API and learn techniques to handle real-time challenges in WebDriver. You'll perform different types of testing, such as cross-browser testing, load testing, and mobile testing. Finally, you will also be introduced to data-driven testing, using TestNG to create your own automation framework.By the end of this Learning Path, you'll be able to design your own automation testing framework and perform data-driven testing with Selenium WebDriver. This Learning Path includes content from the following Packt products: * Selenium WebDriver 3 Practical Guide - Second Edition by Unmesh Gundecha * Selenium Framework Design in Data-Driven Testing by Carl Cocchiaro What you will learn * Use different mobile and desktop browser platforms with Selenium 3 * Use the Actions API for performing various keyboard and mouse actions * Design the Selenium Driver Class for local, remote, and third-party grid support * Build page object classes with the Selenium Page Object Model * Develop data-driven test classes using the TestNG framework * Encapsulate data using the JSON protocol * Build a Selenium Grid for RemoteWebDriver testing * Build and use utility classes in synchronization, file I/O, reporting and test listener classes Who this book is for This Learning Path is ideal for software quality assurance/testing professionals, software project managers, or software developers interested in using Selenium for testing their applications. Professionals responsible for designing and building enterprise-based testing frameworks will also find this Learning Path useful. Prior programming experience in Java are TestNG is necessary.

Leverage the power of Python, Ansible and other network automation tools to make your network robust and more secure Key Features *Get introduced to the concept of network automation with relevant use cases *Apply Continuous Integration and DevOps to improve your network performance *Implement effective automation using tools such as Python, Ansible, and more Book Description Network automation is the use of IT controls to supervise and carry out everyday network management functions. It plays a key role in network virtualization technologies and network functions. The book starts by providing an introduction to network automation, and its applications, which include integrating DevOps tools to automate the network efficiently. It then guides you through different network automation tasks and covers various data digging and performing tasks such as ensuring golden state configurations using templates, interface parsing. This book also focuses on Intelligent Operations using Artificial Intelligence and troubleshooting using chatbots and voice commands. The book then moves on to the use of Python and the management of SSH keys for machine-to-machine (M2M) communication, all followed by practical use cases. The book also covers the importance of Ansible for network automation, including best practices in automation; ways to test automated networks using tools such as Puppet, SaltStack, and Chef; and other important techniques. Through practical use-cases and examples, this book will acquaint you with the various aspects of network automation. It will give you the solid foundation you need to automate your own network without any hassle. What you will learn *Get started with the fundamental concepts of network automation *Perform intelligent data mining and remediation based on triggers *Understand how AIOps works in operations *Trigger automation through data factors *Improve your data center's robustness and security through data digging *Get access infrastructure through API Framework for chatbot and voice interactive troubleshootings *Set up communication with SSH-based devices using Netmiko Who this book is for If you are a network engineer or a DevOps professional looking for an extensive guide to help you automate and manage your network efficiently, then this book is for you. No prior experience with network automation is required to get started, however you will need some exposure to Python programming to get the most out of this book.

Learn how some Go-specific language features help to simplify building web scrapers along with common pitfalls and best practices regarding web scraping. Key Features * Use Go libraries like Goquery and Colly to scrape the web * Common pitfalls and best practices to effectively scrape and crawl * Learn how to scrape using the Go concurrency model Book Description Web scraping is the process of extracting information from the web using various tools that perform scraping and crawling. Go is emerging as the language of choice for scraping using a variety of libraries. This book will quickly explain to you, how to scrape data data from various websites using Go libraries such as Colly and Goquery. The book starts with an introduction to the use cases of building a web scraper and the main features of the Go programming language, along with setting up a Go environment. It then moves on to HTTP requests and responses and talks about how Go handles them. You will also learn about a number of basic web scraping etiquettes. You will be taught how to navigate through a website, using a breadth-first and then a depth-first search, as well as find and follow links. You will get to know about the ways to track history in order to avoid loops and to protect your web scraper using proxies. Finally the book will cover the Go concurrency model, and how to run scrapers in parallel, along with large-scale distributed web scraping. What you will learn * Implement Cache-Control to avoid unnecessary network calls * Coordinate concurrent scrapers * Design a custom, larger-scale scraping system * Scrape basic HTML pages with Colly and JavaScript pages with chromedp * Discover how to search using the "strings" and "regexp" packages * Set up a Go development environment * Retrieve information from an HTML document * Protect your web scraper from being blocked by using proxies * Control web browsers to scrape JavaScript sites Who this book is for Data scientists, and web developers with a basic knowledge of Golang wanting to collect web data and analyze them for effective reporting and visualization.

Discover the power of functional programming, lazy evaluation, monads, concurrency, and immutability to create succinct and expressive implementations Key Features * Get a solid understanding of how to apply functional programming concepts in TypeScript * Explore TypeScript runtime features such as event loop, closures, and Prototypes * Gain deeper knowledge on the pros and cons of TypeScript Book Description Functional programming is a powerful programming paradigm that can help you to write better code. However, learning functional programming can be complicated, and the existing literature is often too complex for beginners. This book is an approachable introduction to functional programming and reactive programming with TypeScript for readers without previous experience in functional programming with JavaScript, TypeScript , or any other programming language. The book will help you understand the pros, cons, and core principles of functional programming in TypeScript. It will explain higher order functions, referential transparency, functional composition, and monads with the help of effective code examples. Using TypeScript as a functional programming language, you’ll also be able to brush up on your knowledge of applying functional programming techniques, including currying, laziness, and immutability, to real-world scenarios. By the end of this book, you will be confident when it comes to using core functional and reactive programming techniques to help you build effective applications with TypeScript. What you will learn * Understand the pros and cons of functional programming * Delve into the principles, patterns, and best practices of functional and reactive programming * Use lazy evaluation to improve the performance of applications * Explore functional optics with Ramda * Gain insights into category theory functional data structures such as Functors and Monads * Use functions as values, so that they can be passed as arguments to other functions Who this book is for This book is designed for readers with no prior experience of functional programming with JavaScript, TypeScript or any other programming language. Some familiarity with TypeScript and web development is a must to grasp the concepts in the book easily.