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Podcasts
D3.js 4.x Data Visualization. Learn to visualize your data with JavaScript - Third Edition
Aendrew Rininsland, Swizec Teller
Want to get started with impressive interactive visualizations and implement them in your daily tasks? This book offers the perfect solution-D3.js. It has emerged as the most popular tool for data visualization. This book will teach you how to implement the features of the latest version of D3 while writing JavaScript using the newest tools and techniqueYou will start by setting up the D3 environment and making your first basic bar chart. You will then build stunning SVG and Canvas-based data visualizations while writing testable, extensible code,as accurate and informative as it is visually stimulating. Step-by-step examples walk you through creating, integrating, and debugging different types of visualization and will have you building basic visualizations (such as bar, line, and scatter graphs) in no time.By the end of this book, you will have mastered the techniques necessary to successfully visualize data and will be ready to use D3 to transform any data into an engaging and sophisticated visualization.
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 book, you will have learned the art of unsupervised learning for different real-world challenges.
Azure Resource Manager (ARM) templates are declarations of Azure resources in the JSON format to provision and maintain them using infrastructure as code. This book gives practical solutions and examples for provisioning and managing various Azure services using ARM templates.The book starts with an understanding of infrastructure as code, a refresher on JSON, and then moves on to explain the fundamental concepts of ARM templates. Important concepts like iteration, conditional evaluation, security, usage of expressions, and functions will be covered in detail. You will use linked and nested templates to create modular ARM templates. You will see how to create multiple instances of the same resources, how to nest and link templates, and how to establish dependencies between them. You will also learn about implementing design patterns, secure template design, the unit testing of ARM templates, and adopting best practices.By the end of this book, you will understand the entire life cycle of ARM templates and their testing, and be able to author them for complex deployments.
AWS Lambda Quick Start Guide. Learn how to build and deploy serverless applications on AWS
AWS Lambda is a part of AWS that lets you run your code without provisioning or managing servers. This enables you to deploy applications and backend services that operate with no upfront cost. This book gets you up to speed on how to build scalable systems and deploy serverless applications with AWS Lambda.The book starts with the fundamental concepts of AWS Lambda, and then teaches you how to combine your applications with other AWS services, such as AmazonAPI Gateway and DynamoDB. This book will also give a quick walk through on how to use the Serverless Framework to build larger applications that can structure code or autogenerate boilerplate code that can be used to get started quickly for increased productivity.Toward the end of the book, you will learn how to write, run, and test Lambda functions using Node.js, Java, Python, and C#.
Rust High Performance. Learn to skyrocket the performance of your Rust applications
This book teaches you how to optimize the performance of your Rust code so that it is at the same level as languages such as C/C++. You'll understand and fi x common pitfalls, learn how to improve your productivity by using metaprogramming, and speed up your code. You will master the features of the language, which will make you stand out, and use them to greatly improve the efficiency of your algorithms. The book begins with an introduction to help you identify bottlenecks when programming in Rust. We highlight common performance pitfalls, along with strategies to detect and resolve these issues early. We move on to mastering Rust's type system, which will enable us to optimize both performance and safety at compile time. You will learn how to effectively manage memory in Rust, mastering the borrow checker. We move on to measuring performance and you will see how this affects the way you write code. Moving forward, you will perform metaprogramming in Rust to boost the performance of your code and your productivity. Finally, you will learn parallel programming in Rust, which enables efficient and faster execution by using multithreading and asynchronous programming.
Robotic Process Automation (RPA) enables automating business processes using software robots. Software robots interpret, trigger responses, and communicate with other systems just like humans do. Robotic processes and intelligent automation tools can help businesses improve the effectiveness of services faster and at a lower cost than current methods.This book is the perfect start to your automation journey, with a special focus on one of the most popular RPA tools: UiPath.Learning Robotic Process Automation takes you on a journey from understanding the basics of RPA to advanced implementation techniques. You will become familiar with the UiPath interface and learn about its workflow. Once you are familiar with the environment, we will get hands-on with automating applications such as Excel, SAP, Windows and web applications, screen and web scraping, working with user events, and we'll cover exceptions and debugging. By the end of the book, you'll not only be able to build your first software robot, but you'll also wire it up to perform various automation tasks with the help of best practices for robot deployment.
This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach.We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data.Later, we will learn how to classify and detect objects in images. We will also learn to use transfer learning for multiple applications, including a self-driving car using Convolutional Neural Networks.We will generate images while leveraging GANs and also by performing image encoding. Additionally, we will perform text analysis using word vector based techniques. Later, we will use Recurrent Neural Networks and LSTM to implement chatbot and Machine Translation systems.Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game.By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter.
Matthew Lamons, Rahul Kumar, Abhishek Nagaraja
Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier.Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system.Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects.By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way