Applied Architecture Patterns on the Microsoft Platform. Work with various Microsoft technologies using Applied Architecture Patterns

Andre Dovgal, Dmitri Olechko, Gregor Noriskin

Applied Computational Thinking with Python. Algorithm design for complex real-world problems - Second Edition

Sofía De Jesús, Dayrene Martinez

Computational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence.This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions.By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.

Applied Computational Thinking with Python. Design algorithmic solutions for complex and challenging real-world problems

Sofía De Jesús, Dayrene Martinez

Computational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence.This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions.By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.

Applied Data Science with Python and Jupyter. Use powerful industry-standard tools to unlock new, actionable insights from your data

Alex Galea

Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations.

Applied Data Visualization with R and ggplot2. Create useful, elaborate, and visually appealing plots

Dr. Tania Moulik

Applied Data Visualization with R and ggplot2 introduces you to the world of data visualization by taking you through the basic features of ggplot2. To start with, you’ll learn how to set up the R environment, followed by getting insights into the grammar of graphics and geometric objects before you explore the plotting techniques.You’ll discover what layers, scales, coordinates, and themes are, and study how you can use them to transform your data into aesthetical graphs. Once you’ve grasped the basics, you’ll move on to studying simple plots such as histograms and advanced plots such as superimposing and density plots. You’ll also get to grips with plotting trends, correlations, and statistical summaries.By the end of this book, you’ll have created data visualizations that will impress your clients.

Applied Deep Learning and Computer Vision for Self-Driving Cars. Build autonomous vehicles using deep neural networks and behavior-cloning techniques

Sumit Ranjan, Dr. S. Senthamilarasu

Thanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars. Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving.By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries.

Applied Deep Learning on Graphs. Leverage graph data for business applications using specialized deep learning architectures

Lakshya Khandelwal, Subhajoy Das

With their combined expertise spanning cutting-edge AI product development at industry giants such as Walmart, Adobe, Samsung, and Arista Networks, Lakshya and Subhajoy provide real-world insights into the transformative world of graph neural networks (GNNs).This book demystifies GNNs, guiding you from foundational concepts to advanced techniques and real-world applications. You’ll see how graph data structures power today’s interconnected world, why specialized deep learning approaches are essential, and how to address challenges with existing methods. You’ll start by dissecting early graph representation techniques such as DeepWalk and node2vec. From there, the book takes you through popular GNN architectures, covering graph convolutional and attention networks, autoencoder models, LLMs, and technologies such as retrieval augmented generation on graph data. With a strong theoretical grounding, you’ll seamlessly navigate practical implementations, mastering the critical topics of scalability, interpretability, and application domains such as NLP, recommendations, and computer vision.By the end of this book, you’ll have mastered the underlying ideas and practical coding skills needed to innovate beyond current methods and gained strategic insights into the future of GNN technologies.

Applied Deep Learning with Keras. Solve complex real-life problems with the simplicity of Keras

Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme

Though designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code.Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the book guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You’ll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you’ll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you’ll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you’ll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model.By the end of this book, you will have the skills you need to use Keras when building high-level deep neural networks.

Applied Deep Learning with Python. Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions

Alex Galea, Luis Capelo

Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before you train your first predictive model. You’ll then explore a variety of approaches to classification such as support vector networks, random decision forests and k-nearest neighbors to build on your knowledge before moving on to advanced topics.After covering classification, you’ll go on to discover ethical web scraping and interactive visualizations, which will help you professionally gather and present your analysis. Next, you’ll start building your keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. You’ll then be guided through a trained neural network, which will help you explore common deep learning network architectures (convolutional, recurrent, and generative adversarial networks) and deep reinforcement learning. Later, you’ll delve into model optimization and evaluation. You’ll do all this while working on a production-ready web application that combines TensorFlow and Keras to produce meaningful user-friendly results.By the end of this book, you’ll be equipped with the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively.

Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems

David S. Jordan

Data scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python.Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries.By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.

Applied Machine Learning and High-Performance Computing on AWS. Accelerate the development of machine learning applications following architectural best practices

Mani Khanuja, Farooq Sabir, Shreyas Subramanian, Trenton Potgieter

Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles.This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you’ll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases.By the end of this book, you’ll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle.

Applied Machine Learning Explainability Techniques. Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more

Aditya Bhattacharya

Explainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases.Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin by gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users.By the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered.

Applied Machine Learning for Healthcare and Life Sciences Using AWS. Transformational AI implementations for biotech, clinical, and healthcare organizations

Ujjwal Ratan

While machine learning is not new, it's only now that we are beginning to uncover its true potential in the healthcare and life sciences industry. The availability of real-world datasets and access to better compute resources have helped researchers invent applications that utilize known AI techniques in every segment of this industry, such as providers, payers, drug discovery, and genomics.This book starts by summarizing the introductory concepts of machine learning and AWS machine learning services. You’ll then go through chapters dedicated to each segment of the healthcare and life sciences industry. Each of these chapters has three key purposes -- First, to introduce each segment of the industry, its challenges, and the applications of machine learning relevant to that segment. Second, to help you get to grips with the features of the services available in the AWS machine learning stack like Amazon SageMaker and Amazon Comprehend Medical. Third, to enable you to apply your new skills to create an ML-driven solution to solve problems particular to that segment. The concluding chapters outline future industry trends and applications.By the end of this book, you’ll be aware of key challenges faced in applying AI to healthcare and life sciences industry and learn how to address those challenges with confidence.

Applied Network Security. Proven tactics to detect and defend against all kinds of network attack

Arthur Salmon, Michael McLafferty, Warun Levesque

Computer networks are increasing at an exponential rate and the most challenging factor organisations are currently facing is network security. Breaching a network is not considered an ingenious effort anymore, so it is very important to gain expertise in securing your network.The book begins by showing you how to identify malicious network behaviour and improve your wireless security. We will teach you what network sniffing is, the various tools associated with it, and how to scan for vulnerable wireless networks. Then we’ll show you how attackers hide the payloads and bypass the victim’s antivirus. Furthermore, we’ll teach you how to spoof IP / MAC address and perform an SQL injection attack and prevent it on your website. We will create an evil twin and demonstrate how to intercept network traffic. Later, you will get familiar with Shodan and Intrusion Detection and will explore the features and tools associated with it. Toward the end, we cover tools such as Yardstick, Ubertooth, Wifi Pineapple, and Alfa used for wireless penetration testing and auditing. This book will show the tools and platform to ethically hack your own network whether it is for your business or for your personal home Wi-Fi.

Applied SOA Patterns on the Oracle Platform. Fuse together your pragmatic Oracle experience with abstract SOA patterns with this practical guide

Sergey Popov

Applied Supervised Learning with Python. Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

Benjamin Johnston, Ishita Mathur

Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support.With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data.By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!