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Deep Learning in Quantitative Finance
The complete and practical guide to one of the hottest topics in quantitative finance Deep learning, that is, the use of deep neural networks, is now one of the hottest topics amongst quantitative analysts.Deep Learning in Quantitative Finance provides a comprehensive treatment of deep learning and describes a wide range of applications in mainstream quantitative finance.Inside, you’ll find over ten chapters which apply deep learning to multiple use cases across quantitative finance.You’ll also gain access to a companion site containing a set of Jupyter notebooks, developed by the author, that use Python to illustrate the examples in the text.Readers will be able to work through these examples directly.This book is a complete resource on how deep learning is used in quantitative finance applications.It introduces the basics of neural networks, including feedforward networks, optimization, and training, before proceeding to cover more advanced topics.You’ll also learn about the most important software frameworks.The book then proceeds to cover the very latest deep learning research in quantitative finance, including approximating derivative values, volatility models, credit curve mapping, generating realistic market data, and hedging.The book concludes with a look at the potential for quantum deep learning and the broader implications deep learning has for quantitative finance and quantitative analysts.Covers the basics of deep learning and neural networks, including feedforward networks, optimization and training, and regularization techniquesOffers an understanding of more advanced topics like CNNs, RNNs, autoencoders, generative models including GANs and VAEs, and deep reinforcement learningDemonstrates deep learning application in quantitative finance through case studies and hands-on applications via the companion websiteIntroduces the most important software frameworks for applying deep learning within finance This book is perfect for anyone engaged with quantitative finance who wants to get involved in a subject that is clearly going to be hugely influential for the future of finance.
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Learning Technology : A Complete Guide for Learning Professionals
Learning technology is now an integral part of all learning and development activity.Understanding what these technologies are, how they work and their aims is key to successful L&D practice.Learning Technology is written by a leading voice in the learning tech industry.It explains the history of learning tech, its aims and how it is the fundamental technology that has driven learning, culture and progress.This book covers everything from writing to printing, broadcast media, teaching technology as well as detailed discussion of learning management systems (LMSs), learning experience platforms (LXPs) and learning record stores (LRSs).It also highlights the importance of data and analytics and covers the latest developments in the learning technology space including artificial intelligence, virtual reality and the metaverse.Learning Technology helps L&D professionals assess and better understand learning platforms and teaching technologies, both past and present. it supports this by evaluating the benefits of each technology.It also provides insights into the future of work and learning and offers a comprehensive overview and detailed exploration of the topic.
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Machine Learning and AI in Finance
The significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events.During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems.Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables.The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions.This volume proposes an optimal model for the volatility smile, for modelling high-frequency liquidity demand and supply and for the simulation of market microstructure features.Other new AI developments explored in this book includes building a universal model for a large number of stocks, developing predictive models based on the average price of the crowd, forecasting the stock price using the attention mechanism in a neural network, clustering multivariate time series into different market states, proposing a multivariate distance nonlinear causality test and filtering out false investment strategies with an unsupervised learning algorithm. Machine Learning and AI in Finance explores the most recent advances in the application of innovative machine learning and artificial intelligence models to predict financial time series, to simulate the structure of the financial markets, to explore nonlinear causality models, to test investment strategies and to price financial options. The chapters in this book were originally published as a special issue of the Quantitative Finance journal.
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Deep Learning for Finance : Creating Machine & Deep Learning Models for Trading in Python
Deep learning is rapidly gaining momentum in the world of finance and trading.But for many professional traders, this sophisticated field has a reputation for being complex and difficult.This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on machine learning and reinforcement learning. Sofien Kaabar—financial author, trading consultant, and institutional market strategist—introduces deep learning strategies that combine technical and quantitative analyses.By fusing deep learning concepts with technical analysis, this unique book presents outside-the-box ideas in the world of financial trading.This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization. Understand and create machine learning and deep learning modelsExplore the details behind reinforcement learning and see how it's used in time seriesUnderstand how to interpret performance evaluation metricsExamine technical analysis and learn how it works in financial marketsCreate technical indicators in Python and combine them with ML models for optimizationEvaluate the models' profitability and predictability to understand their limitations and potential
Price: 55.99 £ | Shipping*: 0.00 £
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Does music help with learning?
Yes, music can help with learning in several ways. It has been shown to improve mood and reduce stress, which can create a more conducive environment for learning. Additionally, certain types of music, such as classical or instrumental, can help improve focus and concentration. Music can also aid in memory retention and recall, as it can create strong associations with the material being learned. However, it is important to note that the effects of music on learning can vary from person to person, and some individuals may find it distracting.
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How can one best start learning design?
One of the best ways to start learning design is by taking online courses or enrolling in a design program at a college or university. It's also helpful to practice regularly and seek feedback from experienced designers. Additionally, studying the work of established designers and staying updated on design trends can provide valuable insights. Finally, getting hands-on experience through internships or freelance projects can help to apply and refine newly acquired skills.
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Does internal music disturb reading and learning?
Internal music can be distracting for some people while reading and learning, as it may compete for attention with the task at hand. However, for others, internal music can actually enhance focus and concentration. It ultimately depends on the individual and their ability to manage internal distractions. If internal music is found to be disruptive, it may be helpful to find a quiet environment for reading and learning.
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How do I start learning music theory?
To start learning music theory, it is recommended to begin with the basics such as understanding the musical alphabet, scales, and key signatures. You can also familiarize yourself with basic music notation and terminology. There are many online resources, books, and courses available to help you learn music theory at your own pace. It is also beneficial to practice applying what you learn by playing an instrument or composing music.
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Machine Learning in Finance : From Theory to Practice
This book introduces machine learning methods in finance.It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making.With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry.This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications.The first presents supervised learning for cross-sectional data from both a Bayesianand frequentist perspective.The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling.The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling.Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management.Python code examples are provided to support the readers' understanding of the methodologies and applications.The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors.As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likelyto emerge as important methodologies for machine learning in finance.
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Foundations of Reinforcement Learning with Applications in Finance
Foundations of Reinforcement Learning with Applications in Finance aims to demystify Reinforcement Learning, and to make it a practically useful tool for those studying and working in applied areas — especially finance. Reinforcement Learning is emerging as a powerful technique for solving a variety of complex problems across industries that involve Sequential Optimal Decisioning under Uncertainty.Its penetration in high-profile problems like self-driving cars, robotics, and strategy games points to a future where Reinforcement Learning algorithms will have decisioning abilities far superior to humans.But when it comes getting educated in this area, there seems to be a reluctance to jump right in, because Reinforcement Learning appears to have acquired a reputation for being mysterious and technically challenging. This book strives to impart a lucid and insightful understanding of the topic by emphasizing the foundational mathematics and implementing models and algorithms in well-designed Python code, along with robust coverage of several financial trading problems that can be solved with Reinforcement Learning.This book has been created after years of iterative experimentation on the pedagogy of these topics while being taught to university students as well as industry practitioners. Features Focus on the foundational theory underpinning Reinforcement Learning and software design of the corresponding models and algorithmsSuitable as a primary text for courses in Reinforcement Learning, but also as supplementary reading for applied/financial mathematics, programming, and other related coursesSuitable for a professional audience of quantitative analysts or data scientistsBlends theory/mathematics, programming/algorithms and real-world financial nuances while always striving to maintain simplicity and to build intuitive understanding To access the code base for this book, please go to: https://github.com/TikhonJelvis/RL-book
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Introduction To Machine Learning In Quantitative Finance, An
In today's world, we are increasingly exposed to the words 'machine learning' (ML), a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation.Over the past few years, ML has gradually permeated the financial sector, reshaping the landscape of quantitative finance as we know it.An Introduction to Machine Learning in Quantitative Finance aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data.In this book the authorsFeatured with the balance of mathematical theorems and practical code examples of ML, this book will help you acquire an in-depth understanding of ML algorithms as well as hands-on experience.After reading An Introduction to Machine Learning in Quantitative Finance, ML tools will not be a black box to you anymore, and you will feel confident in successfully applying what you have learnt to empirical financial data!The Python codes contained within An Introduction to Machine Learning in Quantitative Finance have been made publicly available on the author's GitHub: https://github.com/deepintomlf/mlfbook.git
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MOOCs, High Technology, and Higher Learning
In MOOCs, High Technology, and Higher Learning, Robert A Rhoads places the OpenCourseWare (OCW) movement into the larger context of a revolution in educational technology.In doing so, he seeks to bring greater balance to increasingly polarized discussions of massively open online courses (MOOCs) and show their ongoing relevance to reforming higher education and higher learning.Rhoads offers a provocative analysis of a particular moment in history when cultural, political, and economic forces came together with evolving teaching and learning technologies to bring about the MOOC.He argues persuasively that the OCW and MOOC movements have had a significant impact on the digitalization of knowledge and that they have helped expand the ways students and teachers interact and develop ideas collaboratively.He also critically analyzes the extensive media coverage of MOOCs while examining empirical studies of MOOC content delivery, the organizational system supporting the OCW/MOOC movement, and faculty labor concerns.Too often, technology advocates champion the MOOC movement as a solution to higher education's challenges without recognizing the pedagogical, social, and economic costs. MOOCs, High Technology, and Higher Learning challenges many of the democratic claims made by MOOC advocates, pointing to vast inequities in the ways MOOCs are presented as an alternative to brick-and-mortar access for low-income populations.This book offers a clear-eyed perspective on the potential and peril of this new form of education.
Price: 26.50 £ | Shipping*: 3.99 £
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What is better: music school or self-learning?
The answer to whether music school or self-learning is better depends on individual preferences and goals. Music school can provide structured learning, access to experienced teachers, and opportunities for collaboration with other musicians. On the other hand, self-learning allows for flexibility in scheduling and content, as well as the ability to focus on personal interests. Ultimately, the best approach may involve a combination of both, utilizing the resources and guidance of music school while also exploring and developing skills through self-learning.
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What is better: Music school or self-learning?
The answer to whether music school or self-learning is better depends on individual preferences and goals. Music school can provide structured learning, access to experienced teachers, and opportunities for collaboration with other musicians. On the other hand, self-learning allows for flexibility in scheduling, personalized learning pace, and the freedom to explore various musical styles without constraints. Ultimately, the best approach may be a combination of both, where one can benefit from the structure of music school while also supplementing their learning with self-study and exploration.
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Is it too late to start learning music?
It is never too late to start learning music. People of all ages can begin learning an instrument or taking music lessons. While starting at a younger age may provide more time to develop skills, adults can still make significant progress and find enjoyment in learning music. The key is to have dedication, patience, and a willingness to practice regularly. Whether it's for personal enjoyment or a new career path, it's never too late to start learning music.
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Why deep learning compared to machine learning?
Deep learning is a subset of machine learning that uses neural networks to learn from data. It is more powerful than traditional machine learning techniques because it can automatically discover and learn from complex patterns and features in the data without the need for explicit feature engineering. Deep learning can handle large amounts of data and is capable of learning from unstructured data such as images, audio, and text, making it more versatile and effective for a wide range of applications. Additionally, deep learning models can continuously improve their performance with more data, making them more adaptable and scalable compared to traditional machine learning models.
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