Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business challenges. This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark. Machine Learning with PySpark shows you how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest. You’ll also see unsupervised machine learning models such as K-means and hierarchical clustering. A major portion of the book focuses on feature engineering to create useful features with PySpark to train the machine learning models. The natural language processing section covers text processing, text mining, and embedding for classification. After reading this book, you will understand how to use PySpark’s machine learning library to build and train various machine learning models. Additionally you’ll become comfortable with related PySpark components, such as data ingestion, data processing, and data analysis, that you can use to develop data-driven intelligent applications. What You Will LearnBuild a spectrum of supervised and unsupervised machine learning algorithms Implement machine learning algorithms with Spark MLlib libraries Develop a recommender system with Spark MLlib libraries Handle issues related to feature engineering, class balance, bias and variance, and cross validation for building an optimal fit model Who This Book Is For Data science and machine learning professionals.
Leverage machine and deep learning models to build applications on real-time data using PySpark. This book is perfect for those who want to learn to use this language to perform exploratory data analysis and solve an array of business challenges. You'll start by reviewing PySpark fundamentals, such as Spark’s core architecture, and see how to use PySpark for big data processing like data ingestion, cleaning, and transformations techniques. This is followed by building workflows for analyzing streaming data using PySpark and a comparison of various streaming platforms. You'll then see how to schedule different spark jobs using Airflow with PySpark and book examine tuning machine and deep learning models for real-time predictions. This book concludes with a discussion on graph frames and performing network analysis using graph algorithms in PySpark. All the code presented in the book will be available in Python scripts on Github. What You'll LearnDevelop pipelines for streaming data processing using PySpark Build Machine Learning & Deep Learning models using PySpark latest offerings Use graph analytics using PySpark Create Sequence Embeddings from Text data Who This Book is For Data Scientists, machine learning and deep learning engineers who want to learn and use PySpark for real time analysis on streaming data.
Build and deploy machine learning and deep learning models in production with end-to-end examples. This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes. The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways. What You Will Learn Build, train, and deploy machine learning models at scale using Kubernetes Containerize any kind of machine learning model and run it on any platform using Docker Deploy machine learning and deep learning models using Flask and Streamlit frameworks Who This Book Is For Data engineers, data scientists, analysts, and machine learning and deep learning engineers
Guide covering topics from machine learning, regression models, neural network to tensor flow DESCRIPTION Machine learning is mostly sought in the research field and has become an integral part of many research projects nowadays including commercial applications, as well as academic research. Application of machine learning ranges from finding friends on social networking sites to medical diagnosis and even satellite processing. In this book, we have made an honest effort to make the concepts of machine learning easy and give basic programs in MATLAB right from the installation part. Although the real-time application of machine learning is endless, however, the basic concepts and algorithms are discussed using MATLAB language so that not only graduation students but also researchers are benefitted from it. KEY FEATURES Machine learning in MATLAB using basic concepts and algorithms. Deriving and accessing of data in MATLAB and next, pre-processing and preparation of data. Machine learning workflow for health monitoring. The neural network domain and implementation in MATLAB with explicit explanation of code and results. How predictive model can be improved using MATLAB? MATLAB code for an algorithm implementation, rather than for mathematical formula. Machine learning workflow for health monitoring. WHAT WILL YOU LEARN Pre-requisites to machine learning Finding natural patterns in data Building classification methods Data pre-processing in Python Building regression models Creating neural networks Deep learning WHO THIS BOOK IS FOR The book is basically meant for graduate and research students who find the algorithms of machine learning difficult to implement. We have touched all basic algorithms of machine learning in detail with a practical approach. Primarily, beginners will find this book more effective as the chapters are subdivided in a manner that they find the building and implementation of algorithms in MATLAB interesting and easy at the same time. Table of Contents _1. Ê Ê Pre-requisite to Machine Learning 2. Ê Ê An introduction to Machine Learning 3. Ê Ê Finding Natural Patterns in Data 4. Ê Ê Building Classification Methods 5. Ê Ê Data Pre-Processing in Python 6. Ê Ê Building Regression Models 7. Ê Ê Creating Neural Networks 8. Ê Ê Introduction to Deep Learning
Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. What You'll LearnReview the new features of TensorFlow 2.0Use TensorFlow 2.0 to build machine learning and deep learning models Perform sequence predictions using TensorFlow 2.0Deploy TensorFlow 2.0 models with practical examples Who This Book Is For Data scientists, machine and deep learning engineers.
The purpose of this book is first to study MATLAB programming concepts, then the basic concepts of modeling and simulation analysis, particularly focus on digital communication simulation. The book will cover the topics practically to describe network routing simulation using MATLAB tool. It will cover the dimensions' like Wireless network and WSN simulation using MATLAB, then depict the modeling and simulation of vehicles power network in detail along with considering different case studies. Key features of the book include: Discusses different basics and advanced methodology with their fundamental concepts of exploration and exploitation in NETWORK SIMULATION. Elaborates practice questions and simulations in MATLAB Student-friendly and Concise Useful for UG and PG level research scholar Aimed at Practical approach for network simulation with more programs with step by step comments. Based on the Latest technologies, coverage of wireless simulation and WSN concepts and implementations
Learn to design the Mobile Ad-hoc Networks DESCRIPTION Network Simulation is the most sought after research field, and it has now become an integral part of many research projects like commercial applications and academic research. The networking and communications domain ranges from finding friends on social networking sites to medical diagnosis to smart cities implementation and even satellite processing. In this book, we have made an honest effort to make the concepts of network simulation easyÑall the basics programs are explained in an easy and simple manner in the NS2 simulator, right from the installation part. As the real-time application of networking and communications is endless, the basic concepts and algorithms are discussed using the NS2 simulator so that everyoneÑfrom graduate students to researchersÑcan benefit from this book. KEY FEATURES - Installing NS2 and running simple examples - Creating and incorporating the network module - All the built-in NS2 modules are explained in a comprehensive manner - Details of Network AniMator (NAM) and Xgraph - Simple language, crystal clear approach, and a straightforward comprehensible presentation - The concepts are duly supported by several examples WHAT WILL YOU LEARN Readers will get to know a conspicuous difference of how NS2 is being utilized as a product device in research and business applications. Today, applying network simulations does not require a PhD. Nonetheless, there are a couple of assets out there that completely cover all the essential parts of actualizing networking and communications, without expecting you to take the advanced math courses. We believe that this book will help any individual who needs to apply network simulation, without studying years of analytics, calculus math, and probability hypothesis. WHO THIS BOOK IS FOR The book is basically meant for all those graduate and research students who find the algorithms and protocols of networking and communications difficult to implement. In this book, all basic protocols of networking and simulation are discussed in detail with a practical approach. Primarily, beginners can find this book more effective as the chapters are sub-divided in such a way that they will find building and implementing algorithms in NS2 interesting and easy. Table of Contents 1. Introduction to Network Simulation 2. Tool Command Language 3. Writing and Executing a TCL Scripting with NS2 4. Practical Examples for Wired Program in NS2 5. Mobile Networking in NS2
In 'Train to Pakistan', truth meets fiction as Khushwant Singh recounts the trauma and tragedy of partition through the stories of his characters - the stories that he, his family and friends themselves experienced or saw enacted before their eyes.
This book provides scholars and practitioners in mergers and acquisitions (M&As) with a solid foundation for further research. M&As continue to shape the economic landscape across the globe. While there is already a huge body of scholarly work on the subject, findings appear contradictory and academics and practitioners often struggle to understand what factors make M&As successful. Due to the lack of an agreed-upon definition, research findings appear contradictory, while in fact they are often simply not comparable. To address this, the book rethinks how we measure key umbrella constructs. It specifically focuses on the conceptualization phase of the measurement process, often taken for granted in the current research.
The assassination of Indira Gandhi in 1984 was followed by anti-Sikh riots in Delhi and all over India. Many innocent lives and homes were ruined in this conspiracy with international links, and then followed the aftermath – revenge and more killings. This story zooms into the lives of a few people who were interwoven into the 1984 tragedy directly and inadvertently.
The book examines the market reaction to mergers and acquisitions (M&A) announcements over a period from 2003 to 2015. Mergers and acquisitions continue to be amongst the preferred competitive options available to the companies seeking to grow fast in the rapidly changing global business scenario. M&A as a growth strategy has received attention from developed as well as emerging economies. It has been extensively used by managers as an expansion strategy and also serves as an important instrument for increasing corporate efficiency. Recently, M&A has grown at a rapid pace, creating a need for research to analyze what drives this phenomenon and how it affects firms and markets. As such, this book evaluates the impact of M&A on short-term abnormal returns as well long-term financial performance. It also assesses the management view concerning the motives for undertaking M&A. In addition, the book investigates the corporate governance practices of the acquiring firms and their impact on the short- term as well as long- term performance of those firms.
The quest for energy independence and rising environmental concerns are key drivers in the growing popularity of electric vehicles or EVs - electric and plug-in hybrid cars. Studies indicate that for 90% of the Americans who use their cars to get to work every day, the daily commute distance is less than 50 km - or 30 mi - and, on the average, the commuter car remains parked about 22 h per day. The EVs have in common the batteries, which provide storage capability that can be effectively harnessed when the vehicles are integrated into the grid. The entire concept of using the EVs as a distributed energy resource - load and resource - is known as the vehicle-to-grid or V2G concept. Though I have more than two decades of rendezvous with energy and diversified energy sources to quench the thirst of humanity, my specific interest in electric vehicle started in 2014 when I joined Black & Veatch and got associated with prestigious project of Tesla as strategist and adopt the success model of US market for Asia.Tesla Motors manufactures the Tesla Model S, the all-electric car that won the Motor Trend 2013 Car of the Year award. While developing the car, Tesla launched a program to aggressively deploy high-power, fast-charging stations -- "Superchargers" -- along major travel corridors throughout the United States.Tesla awarded Black & Veatch a contract to design and construct pilot sites in the Supercharger network. The Tesla Supercharger U.S. build-out is the largest project to date for the Black & Veatch team. Services include engineering, site assessment, and permitting and construction services for Tesla's charging stations."It's one thing to build one Supercharger site, but it's a totally different thing to build 100 at a time, or have 40 or 50 in construction at any given time. Black & Veatch brought an ability to be able to expand rapidly, bring on the resources necessary and also manage the construction of a complex project like that - all concurrently." Kevin Kassekert, Director, Supercharger Deployment and Energy Efficiency, Tesla Motors, Inc.It was my absolute privilege to be part of the team of Black & Veatch, who is now a market leader in the design, construction and integration of complex electric vehicle (EV) and hydrogen/fuel cell vehicle (FCV) infrastructure. My journey started with a Big Bang when B&V Chairman Steve Edward pioneered the Chairman's Challenge for new and fresh ideas from offices across the global with the help of an online contest. Absolute delight was my feeling when my first idea on a strategic model of business capture ( I call it Shark Strategy) won the most voted idea of the challenge out of hundreds of ideas submitted by most of the top brains of the 10000 odd employees of the 100 year old firm. It was just the beginning as in the next Chairman's Challenge, I collaborated with others in Kansas HQ to put forth another idea on use of Drone for Industrial Application and Project Management & Monitoring of complex nature like EPC work of intercontinental pipelines or Electric Transmission Lines across the mountains or dense forest like Amazon basin. To my absolute surprise, our team won the top award of the chairman's challenge and each team members were gifted a real Drone costing not less than 15000 INR at that time, but unfortunately it could not be shipped to Mumbai for me as Drones for private applications were banned by government of India. My all other team members sent me pictures of drones awarded to them. Great Memories of Kansas City Baseball match cheering Royals after intensive strategy meetings on future of the company and American Supercharger Infrastructures ( Read Tesla, Volta and other projects).This book is my attempt to help generation next understand and support clean vehicle adoption, advance clean transportation and sustainability.
Damping in Fiber Reinforced Composite Materials starts with an introduction to the basic concepts of damping in composite materials. Methods of modeling damping are then covered, along with recent developments in measuring techniques, both local, like polar scanning and global techniques like the Resonalyser method (based on measuring modal damping ratios of composite material plates). The effect of other factors, such as stress, strain-level, stiffness and frequency that need to be considered when determining damping behavior in composite materials are also discussed in detail. Other chapters present a parametric study of a two-phase composite material using different micromechanical models such as Unified micromechanics, and Hashin and Eshelby’s to predict elastic moduli and loss factors. A bridging model that incorporates the effect of fiber packaging factors is then compared to FEM results. Final sections cover the effect of the interphase on the mechanical properties of the composite, present a nonlinear model for the prediction of damping in viscoelastic materials, and provide practical examples of damping and principles of vibration control. Introduces the basics of damping and dynamic analysis in composite materials Explains damping mechanisms in fiber reinforced composites and modeling principles Covers recent developments in measuring techniques for the identification of damping in composite materials Explains the use of a dynamic mechanical analyzer for predicting damping in composite materials Contains micromechanical studies, modeling of two and three-phase composites, and modeling of non-linear damping Includes experimental results that validate micromechanical models
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