Bøger udgivet af Packt Publishing Limited

This book is designed to show you how to create robust, scalable, highly available and fault-tolerant solutions by learning different aspects of solution architecture and next-generation architecture design in the cloud environment.

Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret textKey Features Understand how to implement deep learning with TensorFlow and Keras Learn the fundamentals of computer vision and image recognition Study the architecture of different neural networksBook DescriptionAre you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout.The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You'll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you'll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you'll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis.By the end of this deep learning book, you'll have learned the skills essential for building deep learning models with TensorFlow and Keras.What you will learn Understand how deep learning, machine learning, and artificial intelligence are different Develop multilayer deep neural networks with TensorFlow Implement deep neural networks for multiclass classification using Keras Train CNN models for image recognition Handle sequence data and use it in conjunction with RNNs Build a GAN to generate high-quality synthesized imagesWho this book is forIf you are interested in machine learning and want to create and train deep learning models using TensorFlow and Keras, this workshop is for you. A solid understanding of Python and its packages, along with basic machine learning concepts, will help you to learn the topics quickly.

Python has a number of powerful packages to help anyone tackle complex mathematical problems in a simple and efficient way. This practical guide explains how to model real-world problems as mathematical objects in Python and how to perform computations, and interpret results. It explores Python lang to solve a variety of math and statistics ...

Unity Virtual Reality Projects walks you through a series of hands-on tutorials and in-depth discussions on using Unity with C#. With its practical and project-based approach, this book will get you up to speed with the specifics of VR development in Unity by building real-world projects that explore various core 3D development concepts.

If you're looking for an alternative to the "P" languages (Perl, PHP, and Python), or want to explore a new paradigm of server-side application development, this Node book is for you. You should have at least a rudimentary understanding of JavaScript and web application development.

This Hands-On Guide on Unity is for the new and existing Unity users to get the most out of Unity Editor, create scripts using C#, delve into graphics, sound, and animations and manipulate physics to create interesting mechanics for your game. All of that will be slowly but immediately applied to a finished real-world game.

Cut through the noise and get real results with this workshop for beginners. Use a project-based approach to exploring machine learning with TensorFlow and Keras.Key Features Understand the nuances of setting up a deep learning programming environment Gain insights into the common components of a neural network and its essential operations Get to grips with deploying a machine learning model as an interactive web application with FlaskBook DescriptionMachine learning gives computers the ability to learn like humans. It is becoming increasingly transformational to businesses in many forms, and a key skill to learn to prepare for the future digital economy.As a beginner, you'll unlock a world of opportunities by learning the techniques you need to contribute to the domains of machine learning, deep learning, and modern data analysis using the latest cutting-edge tools.The Applied TensorFlow and Keras Workshop begins by showing you how neural networks work. After you've understood the basics, you will train a few networks by altering their hyperparameters. To build on your skills, you'll learn how to select the most appropriate model to solve the problem in hand. While tackling advanced concepts, you'll discover how to assemble a deep learning system by bringing together all the essential elements necessary for building a basic deep learning system - data, model, and prediction. Finally, you'll explore ways to evaluate the performance of your model, and improve it using techniques such as model evaluation and hyperparameter optimization.By the end of this book, you'll have learned how to build a Bitcoin app that predicts future prices, and be able to build your own models for other projects.What you will learn Familiarize yourself with the components of a neural network Understand the different types of problems that can be solved using neural networks Explore different ways to select the right architecture for your model Make predictions with a trained model using TensorBoard Discover the components of Keras and ways to leverage its features in your model Explore how you can deal with new data by learning ways to retrain your modelWho this book is forIf you are a data scientist or a machine learning and deep learning enthusiast, who is looking to design, train, and deploy TensorFlow and Keras models into real-world applications, then this workshop is for you. Knowledge of computer science and machine learning concepts and experience in analyzing data will help you to understand the topics explained in this book with ease.

Amazon has come up with Specialty certifications which validates a particular user's expertise that he/she would want to build a career in. This Guide will be a companion to getting skilled with complex and creative security solutions.

This thoroughly revised and expanded second edition demonstrates on over 800 pages how machine learning can add value to algorithmic trading in a practical yet comprehensive way. It has four parts that cover how to work with a diverse set of market, fundamental, and alternative data sources, design ML solutions for real-world trading ...

A beginner's guide to simplifying Extract, Transform, Load (ETL) processes with the help of hands-on tips, tricks, and best practices, in a fun and interactive wayKey Features Explore data wrangling with the help of real-world examples and business use cases Study various ways to extract the most value from your data in minimal time Boost your knowledge with bonus topics, such as random data generation and data integrity checksBook DescriptionWhile a huge amount of data is readily available to us, it is not useful in its raw form. For data to be meaningful, it must be curated and refined.If you're a beginner, then The Data Wrangling Workshop will help to break down the process for you. You'll start with the basics and build your knowledge, progressing from the core aspects behind data wrangling, to using the most popular tools and techniques.This book starts by showing you how to work with data structures using Python. Through examples and activities, you'll understand why you should stay away from traditional methods of data cleaning used in other languages and take advantage of the specialized pre-built routines in Python. Later, you'll learn how to use the same Python backend to extract and transform data from an array of sources, including the internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, the book teaches you how to handle missing or incorrect data, and reformat it based on the requirements from your downstream analytics tool.By the end of this book, you will have developed a solid understanding of how to perform data wrangling with Python, and learned several techniques and best practices to extract, clean, transform, and format your data efficiently, from a diverse array of sources.What you will learn Get to grips with the fundamentals of data wrangling Understand how to model data with random data generation and data integrity checks Discover how to examine data with descriptive statistics and plotting techniques Explore how to search and retrieve information with regular expressions Delve into commonly-used Python data science libraries Become well-versed with how to handle and compensate for missing dataWho this book is forThe Data Wrangling Workshop is designed for developers, data analysts, and business analysts who are looking to pursue a career as a full-fledged data scientist or analytics expert. Although this book is for beginners who want to start data wrangling, prior working knowledge of the Python programming language is necessary to easily grasp the concepts covered here. It will also help to have a rudimentary knowledge of relational databases and SQL.

AI and blockchain are catalyzing the pace of enterprise innovation. This book is a guide to learn the basic concepts, analyze use-cases and implement these technologies across various industries such as healthcare, finance, trade, and supply chain. The book also guides you to build applications using Ethereum, machine learning and MoiBit.

Discover how to leverage Keras, the powerful and easy-to-use open source Python library for developing and evaluating deep learning modelsKey Features Get to grips with various model evaluation metrics, including sensitivity, specificity, and AUC scores Explore advanced concepts such as sequential memory and sequential modeling Reinforce your skills with real-world development, screencasts, and knowledge checksBook DescriptionNew experiences can be intimidating, but not this one! This beginner's guide to deep learning is here to help you explore deep learning from scratch with Keras, and be on your way to training your first ever neural networks.What sets Keras apart from other deep learning frameworks is its simplicity. With over two hundred thousand users, Keras has a stronger adoption in industry and the research community than any other deep learning framework.The Deep Learning with Keras Workshop starts by introducing you to the fundamental concepts of machine learning using the scikit-learn package. After learning how to perform the linear transformations that are necessary for building neural networks, you'll build your first neural network with the Keras library. As you advance, you'll learn how to build multi-layer neural networks and recognize when your model is underfitting or overfitting to the training data. With the help of practical exercises, you'll learn to use cross-validation techniques to evaluate your models and then choose the optimal hyperparameters to fine-tune their performance. Finally, you'll explore recurrent neural networks and learn how to train them to predict values in sequential data.By the end of this book, you'll have developed the skills you need to confidently train your own neural network models.What you will learn Gain insights into the fundamentals of neural networks Understand the limitations of machine learning and how it differs from deep learning Build image classifiers with convolutional neural networks Evaluate, tweak, and improve your models with techniques such as cross-validation Create prediction models to detect data patterns and make predictions Improve model accuracy with L1, L2, and dropout regularizationWho this book is forIf you know the basics of data science and machine learning and want to get started with advanced machine learning technologies like artificial neural networks and deep learning, then this is the book for you. To grasp the concepts explained in this deep learning book more effectively, prior experience in Python programming and some familiarity with statistics and logistic regression are a must.

In today's era of AI, accurately interpreting and communicating trustworthy, fair, and secure AI findings have become a crucial skill to master. This book bridges the gap between AI's pitfalls and potential by helping you build the ability to leverage machine learning with Python to visualize and integrate AI.

Learning how to apply unsupervised algorithms on unlabeled datasets from scratch can be easier than you thought with this beginner's workshop, featuring interesting examples and activitiesKey Features Get familiar with the ecosystem of unsupervised algorithms Learn interesting methods to simplify large amounts of unorganized data Tackle real-world challenges, such as estimating the population density of a geographical areaBook DescriptionDo you find it difficult to understand how popular companies like WhatsApp and Amazon find valuable insights from large amounts of unorganized data? The Unsupervised Learning Workshop will give you the confidence to deal with cluttered and unlabeled datasets, using unsupervised algorithms in an easy and interactive manner.The book starts by introducing the most popular clustering algorithms of unsupervised learning. You'll find out how hierarchical clustering differs from k-means, along with understanding how to apply DBSCAN to highly complex and noisy data. Moving ahead, you'll use autoencoders for efficient data encoding.As you progress, you'll use t-SNE models to extract high-dimensional information into a lower dimension for better visualization, in addition to working with topic modeling for implementing natural language processing (NLP). In later chapters, you'll find key relationships between customers and businesses using Market Basket Analysis, before going on to use Hotspot Analysis for estimating the population density of an area.By the end of this book, you'll be equipped with the skills you need to apply unsupervised algorithms on cluttered datasets to find useful patterns and insights.What you will learn Distinguish between hierarchical clustering and the k-means algorithm Understand the process of finding clusters in data Grasp interesting techniques to reduce the size of data Use autoencoders to decode data Extract text from a large collection of documents using topic modeling Create a bag-of-words model using the CountVectorizerWho this book is forIf you are a data scientist who is just getting started and want to learn how to implement machine learning algorithms to build predictive models, then this book is for you. To expedite the learning process, a solid understanding of the Python programming language is recommended, as you'll be editing classes and functions instead of creating them from scratch.

With The Computer Vision Workshop, you'll explore the basic and advanced techniques in video and image processing using OpenCV and Python. It is filled with real-world exercises and activities that will make the learning process easy and enjoyable.

Computer Programming for Absolute Beginners will help you to learn how to program by taking you through the major constructs you will find in any mainstream programming language. Even if you don't plan on becoming a professional programmer, gaining some programming skills can make a big impact on what you can do with your current skill set.

Clean Code in C# addresses the modern-age software development concerns across the various domains such as Windows desktop applications, web applications, cloud applications, and so on. You will be able to understand how to correct the problems such as code inefficiency, redundancy, duplication as a result of bad code.

Learning ArcGIS Pro teaches readers how to successfully use this new powerful desktop GIS application to create maps, perform spatial analysis, and maintain data. Filled with hands-on exercises based on real-world use cases, this book will show you how to use ArcGIS Pro to visualize, analyze and maintain GIS data.

Explore a modern approach to visualizing data with Python and transform large real-world datasets into expressive visual graphics using this beginner-friendly workshopKey Features Discover the essential tools and methods of data visualization Learn to use standard Python plotting libraries such as Matplotlib and Seaborn Gain insights into the visualization techniques of big companiesBook DescriptionDo you want to transform data into captivating images? Do you want to make it easy for your audience to process and understand the patterns, trends, and relationships hidden within your data?The Data Visualization Workshop will guide you through the world of data visualization and help you to unlock simple secrets for transforming data into meaningful visuals with the help of exciting exercises and activities.Starting with an introduction to data visualization, this book shows you how to first prepare raw data for visualization using NumPy and pandas operations. As you progress, you'll use plotting techniques, such as comparison and distribution, to identify relationships and similarities between datasets. You'll then work through practical exercises to simplify the process of creating visualizations using Python plotting libraries such as Matplotlib and Seaborn. If you've ever wondered how popular companies like Uber and Airbnb use geoplotlib for geographical visualizations, this book has got you covered, helping you analyze and understand the process effectively. Finally, you'll use the Bokeh library to create dynamic visualizations that can be integrated into any web page.By the end of this workshop, you'll have learned how to present engaging mission-critical insights by creating impactful visualizations with real-world data.What you will learn Understand the importance of data visualization in data science Implement NumPy and pandas operations on real-life datasets Create captivating data visualizations using plotting libraries Use advanced techniques to plot geospatial data on a map Integrate interactive visualizations to a webpage Visualize stock prices with Bokeh and analyze Airbnb data with MatplotlibWho this book is forThe Data Visualization Workshop is for beginners who want to learn data visualization, as well as developers and data scientists who are looking to enrich their practical data science skills. Prior knowledge of data analytics, data science, and visualization is not mandatory. Knowledge of Python basics and high-school-level math will help you grasp the concepts covered in this data visualization book more quickly and effectively.

With the help of engaging activities, learn how to leverage Amazon Web Services for building serverless intelligent applications that can process information in no timeKey Features Learn how to integrate Amazon's Simple Storage Services with AI and NLP projects Get to grips with serverless computing and its applications Create intelligent applications such as chatbots and image recognition modelsBook DescriptionAre you fascinated with applications like Alexa and Siri and how they accurately process information within seconds before returning accurate results? Are you looking for a practical guide that will teach you how to build intelligent applications that can revolutionize the world of artificial intelligence? The Applied AI and NLP Workshop will take you on a practical journey where you will learn how to build artificial intelligence (AI) and natural language processing (NLP) applications with Amazon Web services (AWS).Starting with an introduction to AI and machine learning, this book will explain how Amazon S3, or Amazon Simple Storage Service, works. You'll then integrate AI with AWS to build serverless services and use Amazon's NLP service Comprehend to perform text analysis on a document. As you advance, the book will help you get to grips with topic modeling to extract and analyze common themes on a set of documents with unknown topics. You'll also work with Amazon Lex to create and customize a chatbot for task automation and use Amazon Rekognition for detecting objects, scenes, and text in images.By the end of The Applied AI and NLP Workshop, you'll be equipped with the knowledge and skills needed to build scalable intelligent applications with AWS.What you will learn Grasp the fundamentals of AI, ML, and AWS Explore the AWS command line, its interface, and its applications Import and export data to Amazon S3 Perform topic modeling on a set of documents to analyze common themes Develop a custom chatbot to get the latest stock market quotes Create a personal call center and connect it to the chatbotWho this book is forIf you are a machine learning enthusiast, data scientist, or programmer who wants to explore AWS's artificial intelligence and machine learning capabilities, this book is for you. Although not necessary, a basic understanding of AI and NLP will assist with grasping key topics quickly.

With expert guidance and real-world examples, The Machine Learning Workshop gets you up and running with programming machine learning algorithms. By showing you how to leverage scikit-learn's flexibility, it teaches you all the skills you need to use machine learning to solve real-world problems.

Windows Server 2019 powers virtually every single large and medium-sized organizations around the glove. This problem-solution guide has over 100 recipes to help you manage simple to complex administrative tasks in Windows Server 2019.

With this hands-on, self-paced guide, you'll explore crucial deep learning topics and discover the structure and syntax of PyTorch. Challenging activities and interactive exercises will keep you motivated and encourage you to build intelligent applications effectively.

This book covers the theory and practice of building data-driven solutions. Includes the end-to-end process, using supervised and unsupervised algorithms. With each algorithm, you will learn the data acquisition and data engineering methods, the apt metrics, and the available hyper-parameters. You will learn how to deploy the models in production.

The Applied Artificial Intelligence Workshop teaches you the ins and outs of machine learning and neural networks from the ground up, using real-world examples. You'll learn to develop AI and ML models using Python, starting with using the minmax algorithm and alpha-beta pruning to create your first game, and ending with classifying images ...

The Applied Data Science Workshop explores the key elements and interesting applications of data science techniques with the help of practical examples and interactive exercises. Following a hands-on approach, it allows you the freedom of analyzing data in the Jupyter Notebook effectively using many diverse open-source Python libraries.

Web Application Security is a relevant aspect nowadays. Many operations and transactions happen every day on the Worldwide Web, often relying on Databases. SQL Injection Strategies will both show you SQL injection in action, alongside defensive measures. The book also deals with SQL Injection scenarios in the IoT and mobile environments

Kubernetes has emerged as a leader among the management platforms for container orchestration. Hands-On Kubernetes on Azure enables you to strengthen your command over the basic as well as advanced functionalities of Kubernetes.

Learn TypeScript 3 by Building Web Applications is a practical guide which gives you hands-on experience with Typescript by guiding you to write multiple modern Web applications. While building these applications from scratch and enhancing them, you will learn the basics as well as advanced concepts of Typescript.