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| jp morgan ai and data science internship interview: Interview Questions and Answers Richard McMunn, 2013-05 |
| jp morgan ai and data science internship interview: Deep Learning and the Game of Go Kevin Ferguson, Max Pumperla, 2019-01-06 Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning |
| jp morgan ai and data science internship interview: Cracking the Coding Interview Gayle Laakmann McDowell, 2011 Now in the 5th edition, Cracking the Coding Interview gives you the interview preparation you need to get the top software developer jobs. This book provides: 150 Programming Interview Questions and Solutions: From binary trees to binary search, this list of 150 questions includes the most common and most useful questions in data structures, algorithms, and knowledge based questions. 5 Algorithm Approaches: Stop being blind-sided by tough algorithm questions, and learn these five approaches to tackle the trickiest problems. Behind the Scenes of the interview processes at Google, Amazon, Microsoft, Facebook, Yahoo, and Apple: Learn what really goes on during your interview day and how decisions get made. Ten Mistakes Candidates Make -- And How to Avoid Them: Don't lose your dream job by making these common mistakes. Learn what many candidates do wrong, and how to avoid these issues. Steps to Prepare for Behavioral and Technical Questions: Stop meandering through an endless set of questions, while missing some of the most important preparation techniques. Follow these steps to more thoroughly prepare in less time. |
| jp morgan ai and data science internship interview: How Smart Machines Think Sean Gerrish, 2018-10-30 Everything you've always wanted to know about self-driving cars, Netflix recommendations, IBM's Watson, and video game-playing computer programs. The future is here: Self-driving cars are on the streets, an algorithm gives you movie and TV recommendations, IBM's Watson triumphed on Jeopardy over puny human brains, computer programs can be trained to play Atari games. But how do all these things work? In this book, Sean Gerrish offers an engaging and accessible overview of the breakthroughs in artificial intelligence and machine learning that have made today's machines so smart. Gerrish outlines some of the key ideas that enable intelligent machines to perceive and interact with the world. He describes the software architecture that allows self-driving cars to stay on the road and to navigate crowded urban environments; the million-dollar Netflix competition for a better recommendation engine (which had an unexpected ending); and how programmers trained computers to perform certain behaviors by offering them treats, as if they were training a dog. He explains how artificial neural networks enable computers to perceive the world—and to play Atari video games better than humans. He explains Watson's famous victory on Jeopardy, and he looks at how computers play games, describing AlphaGo and Deep Blue, which beat reigning world champions at the strategy games of Go and chess. Computers have not yet mastered everything, however; Gerrish outlines the difficulties in creating intelligent agents that can successfully play video games like StarCraft that have evaded solution—at least for now. Gerrish weaves the stories behind these breakthroughs into the narrative, introducing readers to many of the researchers involved, and keeping technical details to a minimum. Science and technology buffs will find this book an essential guide to a future in which machines can outsmart people. |
| jp morgan ai and data science internship interview: Big Data, Big Challenges: A Healthcare Perspective Mowafa Househ, Andre W. Kushniruk, Elizabeth M. Borycki, 2019-02-26 This is the first book to offer a comprehensive yet concise overview of the challenges and opportunities presented by the use of big data in healthcare. The respective chapters address a range of aspects: from health management to patient safety; from the human factor perspective to ethical and economic considerations, and many more. By providing a historical background on the use of big data, and critically analyzing current approaches together with issues and challenges related to their applications, the book not only sheds light on the problems entailed by big data, but also paves the way for possible solutions and future research directions. Accordingly, it offers an insightful reference guide for health information technology professionals, healthcare managers, healthcare practitioners, and patients alike, aiding them in their decision-making processes; and for students and researchers whose work involves data science-related research issues in healthcare. |
| jp morgan ai and data science internship interview: Ace the Data Science Interview Kevin Huo, Nick Singh, 2021 |
| jp morgan ai and data science internship interview: Fifty Challenging Problems in Probability with Solutions Frederick Mosteller, 2012-04-26 Remarkable puzzlers, graded in difficulty, illustrate elementary and advanced aspects of probability. These problems were selected for originality, general interest, or because they demonstrate valuable techniques. Also includes detailed solutions. |
| jp morgan ai and data science internship interview: The Best Book On Investment Banking Careers Donna Khalife, 2012-07-24 Whether you’re an undergraduate prepping for your first internship, or seeking a new career in investment banking, knowing the ins and outs of the industry can help you make your big break. In this eBook, Donna Khalife shares an insider’s perspective to the investment banking industry and helps prepare readers for their chance at landing their dream job. |
| jp morgan ai and data science internship interview: Heard on The Street Timothy Falcon Crack, 2024-08-05 [Warning: Do not buy an old edition of Timothy Crack's books by mistake. Click on the Amazon author page link for a list of the latest editions .] THIS IS A MUST READ! It is the first and the original book of quantitative questions from finance job interviews. Painstakingly revised over 30 years and 25 editions, Heard on The Street has been shaped by feedback from hundreds of readers. With well over 75,000 copies in print, its readership is unmatched by any competing book. The revised 25th edition contains 242 quantitative questions collected from actual job interviews in investment banking, investment management, and options trading. The interviewers use the same questions year-after-year, and here they are with detailed solutions! This edition also includes 267 non-quantitative actual interview questions, giving a total of more than 500 actual finance job interview questions. Questions that appeared in (or are likely to appear in) traditional corporate finance or investment banking job interviews are indicated with a bank symbol in the margin (72 of the 242 quant questions and 196 of the 267 non-quant questions). This makes it easier for corporate finance candidates to go directly to the questions most relevant to them. Most of these questions also appeared in capital markets interviews and quant interviews. So, they should not be skipped over by capital markets or quant candidates unless they are obviously irrelevant. There is also a recently revised section on interview technique based on feedback from interviewers worldwide. The quant questions cover pure quant/logic, financial economics, derivatives, and statistics. They come from all types of interviews (corporate finance, sales and trading, quant research, etc.), and from all levels of interviews (undergraduate, MS, MBA, PhD). The first seven editions of Heard on the Street contained an appendix on option pricing. That appendix was carved out as a standalone book many years ago and it is now available in a recently revised edition: Basic Black-Scholes. Dr. Crack did PhD coursework at MIT and Harvard, and graduated with a PhD from MIT. He has won many teaching awards, and has publications in the top academic, practitioner, and teaching journals in finance. He has degrees/diplomas in Mathematics/Statistics, Finance, Financial Economics and Accounting/Finance. Dr. Crack taught at the university level for over 25 years including four years as a front line teaching assistant for MBA students at MIT, and four years teaching undergraduates, MBAs, and PhDs at Indiana University. He has worked as an independent consultant to the New York Stock Exchange and to a foreign government body investigating wrong doing in the financial markets. He previously held a practitioner job as the head of a quantitative active equity research team at what was the world's largest institutional money manager. |
| jp morgan ai and data science internship interview: Living Mindfully Across the Lifespan J. Kim Penberthy, J. Morgan Penberthy, 2020-11-22 Living Mindfully Across the Lifespan: An Intergenerational Guide provides user-friendly, empirically supported information about and answers to some of the most frequently encountered questions and dilemmas of human living, interactions, and emotions. With a mix of empirical data, humor, and personal insight, each chapter introduces the reader to a significant topic or question, including self-worth, anxiety, depression, relationships, personal development, loss, and death. Along with exercises that clients and therapists can use in daily practice, chapters feature personal stories and case studies, interwoven throughout with the authors’ unique intergenerational perspectives. Compassionate, engaging writing is balanced with a straightforward presentation of research data and practical strategies to help address issues via psychological, behavioral, contemplative, and movement-oriented exercises. Readers will learn how to look deeply at themselves and society, and to apply what has been learned over decades of research and clinical experience to enrich their lives and the lives of others. |
| jp morgan ai and data science internship interview: The Department of Defense Posture for Artificial Intelligence Danielle C. Tarraf, William Shelton, Edward Parker, 2020-01-30 In this report, the authors assess the state of artificial intelligence (AI) relevant to DoD, conduct an independent assessment of the Department of Defense's posture in AI, and put forth a set of recommendations to enhance that posture. |
| jp morgan ai and data science internship interview: Collecting Qualitative Data Greg Guest, Emily E. Namey, Marilyn L. Mitchell, 2013 Provides a very practical and step-by-step guide to collecting and managing qualitative data, |
| jp morgan ai and data science internship interview: The American Psychiatric Association Practice Guideline for the Pharmacological Treatment of Patients With Alcohol Use Disorder American Psychiatric Association, 2018-01-11 Alcohol use disorder (AUD) is a major public health problem in the United States. The estimated 12-month and lifetime prevalence values for AUD are 13.9% and 29.1%, respectively, with approximately half of individuals with lifetime AUD having a severe disorder. AUD and its sequelae also account for significant excess mortality and cost the United States more than $200 billion annually. Despite its high prevalence and numerous negative consequences, AUD remains undertreated. In fact, fewer than 1 in 10 individuals in the United States with a 12-month diagnosis of AUD receive any treatment. Nevertheless, effective and evidence-based interventions are available, and treatment is associated with reductions in the risk of relapse and AUD-associated mortality. The American Psychiatric Association Practice Guideline for the Pharmacological Treatment of Patients With Alcohol Use Disorder seeks to reduce these substantial psychosocial and public health consequences of AUD for millions of affected individuals. The guideline focuses specifically on evidence-based pharmacological treatments for AUD in outpatient settings and includes additional information on assessment and treatment planning, which are an integral part of using pharmacotherapy to treat AUD. In addition to reviewing the available evidence on the use of AUD pharmacotherapy, the guideline offers clear, concise, and actionable recommendation statements, each of which is given a rating that reflects the level of confidence that potential benefits of an intervention outweigh potential harms. The guideline provides guidance on implementing these recommendations into clinical practice, with the goal of improving quality of care and treatment outcomes of AUD. |
| jp morgan ai and data science internship interview: What They Didn't Teach You in School Samir Ranjan Majhi, 2017-05-21 How much of what you learnt in school do you still use? You probably aren't using anything you learnt in Chemistry.How much of what you need to know wasn't taught in school? Why is it that no matter how much you earn, you keep eagerly waiting for your next month's salary? This book is an older version of yourself imparting the life lessons that you learned along the way. |
| jp morgan ai and data science internship interview: Mirror Jeeya Prakash, 2019-07-21 I am messed up, veneer, a little in distress, On Saturdays, I get drunk on my regrets, And to know me more, is to love me less. Is how this young author unravels herself in her maiden attempt at poetic expression. Absorbing the pressures and obstacles faced during her adolescent years, the author found a release of her pent up emotions as she penned down her experiences. Mirror is a collection of her thoughts ideas and expressions during her final years in a convent school, her intermediate from an army school and her journey through the rigorous entrance drills to finally adjusting for B.Tech in a renowned university recently. |
| jp morgan ai and data science internship interview: Suits Nina Godiwalla, 2011-02-28 A fiercely ambitious woman from the Persian-Indian community ventures from Houston to New York to follow her dream of working in the world of banking and finance in pursuit of success, honor, and family pride. |
| jp morgan ai and data science internship interview: STOP, THAT and One Hundred Other Sleep Scales Azmeh Shahid, Kate Wilkinson, Shai Marcu, Colin M Shapiro, 2012-01-06 There are at least four reasons why a sleep clinician should be familiar with rating scales that evaluate different facets of sleep. First, the use of scales facilitates a quick and accurate assessment of a complex clinical problem. In three or four minutes (the time to review ten standard scales), a clinician can come to a broad understanding of the patient in question. For example, a selection of scales might indicate that an individual is sleepy but not fatigued; lacking alertness with no insomnia; presenting with no symptoms of narcolepsy or restless legs but showing clear features of apnea; exhibiting depression and a history of significant alcohol problems. This information can be used to direct the consultation to those issues perceived as most relevant, and can even provide a springboard for explaining the benefits of certain treatment approaches or the potential corollaries of allowing the status quo to continue. Second, rating scales can provide a clinician with an enhanced vocabulary or language, improving his or her understanding of each patient. In the case of the sleep specialist, a scale can help him to distinguish fatigue from sleepiness in a patient, or elucidate the differences between sleepiness and alertness (which is not merely the inverse of the former). Sleep scales are developed by researchers and clinicians who have spent years in their field, carefully honing their preferred methods for assessing certain brain states or characteristic features of a condition. Thus, scales provide clinicians with a repertoire of questions, allowing them to draw upon the extensive experience of their colleagues when attempting to tease apart nuanced problems. Third, some scales are helpful for tracking a patient’s progress. A particular patient may not remember how alert he felt on a series of different stimulant medications. Scale assessments administered periodically over the course of treatment provide an objective record of the intervention, allowing the clinician to examine and possibly reassess her approach to the patient. Finally, for individuals conducting a double-blind crossover trial or a straightforward clinical practice audit, those who are interested in research will find that their own clinics become a source of great discovery. Scales provide standardized measures that allow colleagues across cities and countries to coordinate their practices. They enable the replication of previous studies and facilitate the organization and dissemination of new research in a way that is accessible and rapid. As the emphasis placed on evidence-based care grows, a clinician’s ability to assess his or her own practice and its relation to the wider medical community becomes invaluable. Scales make this kind of standardization possible, just as they enable the research efforts that help to formulate those standards. The majority of Rating Scales in Sleep and Sleep Disorders:100 Scales for Clinical Practice is devoted to briefly discussing individual scales. When possible, an example of the scale is provided so that readers may gain a sense of the instrument’s content. Groundbreaking and the first of its kind to conceptualize and organize the essential scales used in sleep medicine, Rating Scales in Sleep and Sleep Disorders:100 Scales for Clinical Practice is an invaluable resource for all clinicians and researchers interested in sleep disorders. |
| jp morgan ai and data science internship interview: Artificial Intelligence in Healthcare Adam Bohr, Kaveh Memarzadeh, 2020-06-21 Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data |
| jp morgan ai and data science internship interview: Artificial Intelligence in Medicine Lei Xing, Maryellen L. Giger, James K. Min, 2020-09-03 Artificial Intelligence Medicine: Technical Basis and Clinical Applications presents a comprehensive overview of the field, ranging from its history and technical foundations, to specific clinical applications and finally to prospects. Artificial Intelligence (AI) is expanding across all domains at a breakneck speed. Medicine, with the availability of large multidimensional datasets, lends itself to strong potential advancement with the appropriate harnessing of AI. The integration of AI can occur throughout the continuum of medicine: from basic laboratory discovery to clinical application and healthcare delivery. Integrating AI within medicine has been met with both excitement and scepticism. By understanding how AI works, and developing an appreciation for both limitations and strengths, clinicians can harness its computational power to streamline workflow and improve patient care. It also provides the opportunity to improve upon research methodologies beyond what is currently available using traditional statistical approaches. On the other hand, computers scientists and data analysts can provide solutions, but often lack easy access to clinical insight that may help focus their efforts. This book provides vital background knowledge to help bring these two groups together, and to engage in more streamlined dialogue to yield productive collaborative solutions in the field of medicine. - Provides history and overview of artificial intelligence, as narrated by pioneers in the field - Discusses broad and deep background and updates on recent advances in both medicine and artificial intelligence that enabled the application of artificial intelligence - Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach |
| jp morgan ai and data science internship interview: You Don't Know JS: Scope & Closures Kyle Simpson, 2014-03-10 No matter how much experience you have with JavaScript, odds are you don’t fully understand the language. This concise yet in-depth guide takes you inside scope and closures, two core concepts you need to know to become a more efficient and effective JavaScript programmer. You’ll learn how and why they work, and how an understanding of closures can be a powerful part of your development skillset. Like other books in the You Don’t Know JS series, Scope and Closures dives into trickier parts of the language that many JavaScript programmers simply avoid. Armed with this knowledge, you can achieve true JavaScript mastery. Learn about scope, a set of rules to help JavaScript engines locate variables in your code Go deeper into nested scope, a series of containers for variables and functions Explore function- and block-based scope, “hoisting”, and the patterns and benefits of scope-based hiding Discover how to use closures for synchronous and asynchronous tasks, including the creation of JavaScript libraries |
| jp morgan ai and data science internship interview: The 2-Hour Job Search Steve Dalton, 2012-03-06 A job-search manual that gives career seekers a systematic, tech-savvy formula to efficiently and effectively target potential employers and secure the essential first interview. The 2-Hour Job Search shows job-seekers how to work smarter (and faster) to secure first interviews. Through a prescriptive approach, Dalton explains how to wade through the Internet’s sea of information and create a job-search system that relies on mainstream technology such as Excel, Google, LinkedIn, and alumni databases to create a list of target employers, contact them, and then secure an interview—with only two hours of effort. Avoiding vague tips like “leverage your contacts,” Dalton tells job-hunters exactly what to do and how to do it. This empowering book focuses on the critical middle phase of the job search and helps readers bring organization to what is all too often an ineffectual and frustrating process. |
| jp morgan ai and data science internship interview: Dare to Lead Brené Brown, 2018-10-09 #1 NEW YORK TIMES BESTSELLER • Brené Brown has taught us what it means to dare greatly, rise strong, and brave the wilderness. Now, based on new research conducted with leaders, change makers, and culture shifters, she’s showing us how to put those ideas into practice so we can step up and lead. Don’t miss the five-part Max docuseries Brené Brown: Atlas of the Heart! ONE OF BLOOMBERG’S BEST BOOKS OF THE YEAR Leadership is not about titles, status, and wielding power. A leader is anyone who takes responsibility for recognizing the potential in people and ideas, and has the courage to develop that potential. When we dare to lead, we don’t pretend to have the right answers; we stay curious and ask the right questions. We don’t see power as finite and hoard it; we know that power becomes infinite when we share it with others. We don’t avoid difficult conversations and situations; we lean into vulnerability when it’s necessary to do good work. But daring leadership in a culture defined by scarcity, fear, and uncertainty requires skill-building around traits that are deeply and uniquely human. The irony is that we’re choosing not to invest in developing the hearts and minds of leaders at the exact same time as we’re scrambling to figure out what we have to offer that machines and AI can’t do better and faster. What can we do better? Empathy, connection, and courage, to start. Four-time #1 New York Times bestselling author Brené Brown has spent the past two decades studying the emotions and experiences that give meaning to our lives, and the past seven years working with transformative leaders and teams spanning the globe. She found that leaders in organizations ranging from small entrepreneurial startups and family-owned businesses to nonprofits, civic organizations, and Fortune 50 companies all ask the same question: How do you cultivate braver, more daring leaders, and how do you embed the value of courage in your culture? In Dare to Lead, Brown uses research, stories, and examples to answer these questions in the no-BS style that millions of readers have come to expect and love. Brown writes, “One of the most important findings of my career is that daring leadership is a collection of four skill sets that are 100 percent teachable, observable, and measurable. It’s learning and unlearning that requires brave work, tough conversations, and showing up with your whole heart. Easy? No. Because choosing courage over comfort is not always our default. Worth it? Always. We want to be brave with our lives and our work. It’s why we’re here.” Whether you’ve read Daring Greatly and Rising Strong or you’re new to Brené Brown’s work, this book is for anyone who wants to step up and into brave leadership. |
| jp morgan ai and data science internship interview: Decode and Conquer Lewis C. Lin, 2013-11-28 Land that Dream Product Manager Job...TODAYSeeking a product management position?Get Decode and Conquer, the world's first book on preparing you for the product management (PM) interview. Author and professional interview coach, Lewis C. Lin provides you with an industry insider's perspective on how to conquer the most difficult PM interview questions. Decode and Conquer reveals: Frameworks for tackling product design and metrics questions, including the CIRCLES Method(tm), AARM Method(tm), and DIGS Method(tm) Biggest mistakes PM candidates make at the interview and how to avoid them Insider tips on just what interviewers are looking for and how to answer so they can't say NO to hiring you Sample answers for the most important PM interview questions Questions and answers covered in the book include: Design a new iPad app for Google Spreadsheet. Brainstorm as many algorithms as possible for recommending Twitter followers. You're the CEO of the Yellow Cab taxi service. How do you respond to Uber? You're part of the Google Search web spam team. How would you detect duplicate websites? The billboard industry is under monetized. How can Google create a new product or offering to address this? Get the Book that's Recommended by Executives from Google, Amazon, Microsoft, Oracle & VMWare...TODAY |
| jp morgan ai and data science internship interview: Textbook of Family Medicine E-Book Robert E. Rakel, 2007-05 This text has been admired for as long as Family Medicine has been a recognized specialty. Edited by the legendary Robert E. Rakel, MD, this superb 7th edition continues to break new ground. Includes materials to help hone your clinical skills and prepare for the ABFP boards and SPEX exams. Highlights especially important points of diagnosis and therapy in the case section of book. Provides Best Evidence Recommendations boxes to promote greater reliability of information. Offers a free CD-rom containing video clips of diabetes testing, stress test and all the illustrations from the book! Contains new chapters on complementary and alternative medicine. Takes a fresh new approach to evidence based medicine in clinical practice. Uses a visually appealing, functional 4-color design and a full-color insert. |
| jp morgan ai and data science internship interview: Java Concurrency in Practice Tim Peierls, Brian Goetz, Joshua Bloch, Joseph Bowbeer, Doug Lea, David Holmes, 2006-05-09 Threads are a fundamental part of the Java platform. As multicore processors become the norm, using concurrency effectively becomes essential for building high-performance applications. Java SE 5 and 6 are a huge step forward for the development of concurrent applications, with improvements to the Java Virtual Machine to support high-performance, highly scalable concurrent classes and a rich set of new concurrency building blocks. In Java Concurrency in Practice, the creators of these new facilities explain not only how they work and how to use them, but also the motivation and design patterns behind them. However, developing, testing, and debugging multithreaded programs can still be very difficult; it is all too easy to create concurrent programs that appear to work, but fail when it matters most: in production, under heavy load. Java Concurrency in Practice arms readers with both the theoretical underpinnings and concrete techniques for building reliable, scalable, maintainable concurrent applications. Rather than simply offering an inventory of concurrency APIs and mechanisms, it provides design rules, patterns, and mental models that make it easier to build concurrent programs that are both correct and performant. This book covers: Basic concepts of concurrency and thread safety Techniques for building and composing thread-safe classes Using the concurrency building blocks in java.util.concurrent Performance optimization dos and don'ts Testing concurrent programs Advanced topics such as atomic variables, nonblocking algorithms, and the Java Memory Model |
| jp morgan ai and data science internship interview: Cracking the Data Science Interview Maverick Lin, 2019-12-17 Cracking the Data Science Interview is the first book that attempts to capture the essence of data science in a concise, compact, and clean manner. In a Cracking the Coding Interview style, Cracking the Data Science Interview first introduces the relevant concepts, then presents a series of interview questions to help you solidify your understanding and prepare you for your next interview. Topics include: - Necessary Prerequisites (statistics, probability, linear algebra, and computer science) - 18 Big Ideas in Data Science (such as Occam's Razor, Overfitting, Bias/Variance Tradeoff, Cloud Computing, and Curse of Dimensionality) - Data Wrangling (exploratory data analysis, feature engineering, data cleaning and visualization) - Machine Learning Models (such as k-NN, random forests, boosting, neural networks, k-means clustering, PCA, and more) - Reinforcement Learning (Q-Learning and Deep Q-Learning) - Non-Machine Learning Tools (graph theory, ARIMA, linear programming) - Case Studies (a look at what data science means at companies like Amazon and Uber) Maverick holds a bachelor's degree from the College of Engineering at Cornell University in operations research and information engineering (ORIE) and a minor in computer science. He is the author of the popular Data Science Cheatsheet and Data Engineering Cheatsheet on GCP and has previous experience in data science consulting for a Fortune 500 company focusing on fraud analytics. |
| jp morgan ai and data science internship interview: Numerical Python Robert Johansson, |
| jp morgan ai and data science internship interview: Engineering a Better Future Eswaran Subrahmanian, Toluwalogo Odumosu, Jeffrey Y. Tsao, 2018-11-12 This open access book examines how the social sciences can be integrated into the praxis of engineering and science, presenting unique perspectives on the interplay between engineering and social science. Motivated by the report by the Commission on Humanities and Social Sciences of the American Association of Arts and Sciences, which emphasizes the importance of social sciences and Humanities in technical fields, the essays and papers collected in this book were presented at the NSF-funded workshop ‘Engineering a Better Future: Interplay between Engineering, Social Sciences and Innovation’, which brought together a singular collection of people, topics and disciplines. The book is split into three parts: A. Meeting at the Middle: Challenges to educating at the boundaries covers experiments in combining engineering education and the social sciences; B. Engineers Shaping Human Affairs: Investigating the interaction between social sciences and engineering, including the cult of innovation, politics of engineering, engineering design and future of societies; and C. Engineering the Engineers: Investigates thinking about design with papers on the art and science of science and engineering practice. |
| jp morgan ai and data science internship interview: Dimensions Netra Hirani, 2021-08-17 ‘Dimensions’ teleports you to a poetic crossroad of Desire, Delusions, Denial, Dreams, and Dilemma. Every poem in this book has been painted with colours of different dimensions- some touching your heart, some teasing your mind and some tickling your soul. “It’s an enigma, a paradox, a crossfade of two spaces sashaying past each other.”- Shruti Kohli “An emotion-laden verse-prose anthology that tugs at your heart and makes you smile.”- Chitrangada Mukherjee “Her poems reminded me of my own teenage years.”- Aditya Gautam “This multidimensional ode to myriad moments of life is unencumbered, raw, and soul-stirring. Highly recommended for those that have lost, as well as found.”- Ayan Pal |
| jp morgan ai and data science internship interview: A Practical Guide To Quantitative Finance Interviews Xinfeng Zhou, 2020-05-05 This book will prepare you for quantitative finance interviews by helping you zero in on the key concepts that are frequently tested in such interviews. In this book we analyze solutions to more than 200 real interview problems and provide valuable insights into how to ace quantitative interviews. The book covers a variety of topics that you are likely to encounter in quantitative interviews: brain teasers, calculus, linear algebra, probability, stochastic processes and stochastic calculus, finance and programming. |
| jp morgan ai and data science internship interview: Journal of the National Cancer Institute , 2006 |
| jp morgan ai and data science internship interview: Principles of Financial Engineering Robert Kosowski, Salih N. Neftci, 2014-11-26 Principles of Financial Engineering, Third Edition, is a highly acclaimed text on the fast-paced and complex subject of financial engineering. This updated edition describes the engineering elements of financial engineering instead of the mathematics underlying it. It shows how to use financial tools to accomplish a goal rather than describing the tools themselves. It lays emphasis on the engineering aspects of derivatives (how to create them) rather than their pricing (how they act) in relation to other instruments, the financial markets, and financial market practices. This volume explains ways to create financial tools and how the tools work together to achieve specific goals. Applications are illustrated using real-world examples. It presents three new chapters on financial engineering in topics ranging from commodity markets to financial engineering applications in hedge fund strategies, correlation swaps, structural models of default, capital structure arbitrage, contingent convertibles, and how to incorporate counterparty risk into derivatives pricing. Poised midway between intuition, actual events, and financial mathematics, this book can be used to solve problems in risk management, taxation, regulation, and above all, pricing. A solutions manual enhances the text by presenting additional cases and solutions to exercises. This latest edition of Principles of Financial Engineering is ideal for financial engineers, quantitative analysts in banks and investment houses, and other financial industry professionals. It is also highly recommended to graduate students in financial engineering and financial mathematics programs. - The Third Edition presents three new chapters on financial engineering in commodity markets, financial engineering applications in hedge fund strategies, correlation swaps, structural models of default, capital structure arbitrage, contingent convertibles and how to incorporate counterparty risk into derivatives pricing, among other topics - Additions, clarifications, and illustrations throughout the volume show these instruments at work instead of explaining how they should act - The solutions manual enhances the text by presenting additional cases and solutions to exercises |
| jp morgan ai and data science internship interview: Machine Learning Bookcamp Alexey Grigorev, 2021-11-23 The only way to learn is to practice! In Machine Learning Bookcamp, you''ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you''ve learned in previous chapters. By the end of the bookcamp, you''ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technology Machine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that''s exactly what you''ll be doing in Machine Learning Bookcamp. about the book In Machine Learning Bookcamp you''ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you''ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You''ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you''re done working through these fun and informative projects, you''ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems. what''s inside Code fundamental ML algorithms from scratch Collect and clean data for training models Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images and text Deploy ML models to a production-ready environment about the reader For readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning. |
| jp morgan ai and data science internship interview: Java Programming Interviews Exposed Noel Markham, 2014-02-17 If you are a skilled Java programmer but are concerned about the Java coding interview process, this real-world guide can help you land your next position Java is a popular and powerful language that is a virtual requirement for businesses making use of IT in their daily operations. For Java programmers, this reality offers job security and a wealth of employment opportunities. But that perfect Java coding job won't be available if you can't ace the interview. If you are a Java programmer concerned about interviewing, Java Programming Interviews Exposed is a great resource to prepare for your next opportunity. Author Noel Markham is both an experienced Java developer and interviewer, and has loaded his book with real examples from interviews he has conducted. Review over 150 real-world Java interview questions you are likely to encounter Prepare for personality-based interviews as well as highly technical interviews Explore related topics, such as middleware frameworks and server technologies Make use of chapters individually for topic-specific help Use the appendix for tips on Scala and Groovy, two other languages that run on JVMs Veterans of the IT employment space know that interviewing for a Java programming position isn't as simple as sitting down and answering questions. The technical coding portion of the interview can be akin to a difficult puzzle or an interrogation. With Java Programming Interviews Exposed, skilled Java coders can prepare themselves for this daunting process and better arm themselves with the knowledge and interviewing skills necessary to succeed. |
| jp morgan ai and data science internship interview: Machine Learning with Python for Everyone Mark Fenner, 2019-07-30 The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use. Understand machine learning algorithms, models, and core machine learning concepts Classify examples with classifiers, and quantify examples with regressors Realistically assess performance of machine learning systems Use feature engineering to smooth rough data into useful forms Chain multiple components into one system and tune its performance Apply machine learning techniques to images and text Connect the core concepts to neural networks and graphical models Leverage the Python scikit-learn library and other powerful tools Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| jp morgan ai and data science internship interview: Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021) Rajiv Misra, Rudrapatna K. Shyamasundar, Amrita Chaturvedi, Rana Omer, 2021-09-29 This edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2021) is intended to be used as a reference book for researchers and practitioners in the disciplines of computer science, electronics and telecommunication, information science, and electrical engineering. Machine learning and Big data analytics represent a key ingredients in the industrial applications for new products and services. Big data analytics applies machine learning for predictions by examining large and varied data sets—i.e., big data—to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that can help organizations make more informed business decisions. |
| jp morgan ai and data science internship interview: Humanitarian Military Intervention Taylor B. Seybolt, 2008 The author describes the reasons why humanitarian military interventions succeed or fail, basing his analysis on the interventions carried out in the 1990s in Iraq, Somalia, Bosnia and Herzegovina, Rwanda, Kosovo, and East Timor. |
| jp morgan ai and data science internship interview: The The Modern C# Challenge Rod Stephens, 2018-10-25 Learn advanced C# concepts and techniques such as building caches, cryptography, and parallel programming by solving interesting programming challenges Key FeaturesGain useful insights on advanced C# programming topics and APIsUse locking and cached values to solve parallel problemsTake advantage of .NET's cryptographic tools to encrypt and decrypt stringsBook Description C# is a multi-paradigm programming language. The Modern C# Challenge covers with aspects of the .NET Framework such as the Task Parallel Library (TPL) and CryptoAPI. It also encourages you to explore important programming trade-offs such as time versus space or simplicity. There may be many ways to solve a problem and there is often no single right way, but some solutions are definitely better than others. This book has combined these solutions to help you solve real-world problems with C#. In addition to describing programming trade-offs, The Modern C# Challenge will help you build a useful toolkit of techniques such as value caching, statistical analysis, and geometric algorithms. By the end of this book, you will have walked through challenges in C# and explored the .NET Framework in order to develop program logic for real-world applications. What you will learnPerform statistical calculations such as finding the standard deviationFind combinations and permutationsSearch directories for files matching patterns using LINQ and PLINQFind areas of polygons using geometric operationsRandomize arrays and lists with extension methodsExplore the filesystem to find duplicate filesSimulate complex systems and implement equality in a classUse cryptographic techniques to encrypt and decrypt strings and filesWho this book is for The Modern C# Challenge is for all C# developers of different abilities wanting to solve real-world problems. There are problems for everyone at any level of expertise in C# |
| jp morgan ai and data science internship interview: The School Monthly , 1867 |
| jp morgan ai and data science internship interview: Archaeology, Anthropology, and Interstellar Communication National Aeronautics Administration, Douglas Vakoch, 2014-09-06 Addressing a field that has been dominated by astronomers, physicists, engineers, and computer scientists, the contributors to this collection raise questions that may have been overlooked by physical scientists about the ease of establishing meaningful communication with an extraterrestrial intelligence. These scholars are grappling with some of the enormous challenges that will face humanity if an information-rich signal emanating from another world is detected. By drawing on issues at the core of contemporary archaeology and anthropology, we can be much better prepared for contact with an extraterrestrial civilization, should that day ever come. |
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