Predicting Film Ratings Using Collaborative Filtering And Deep Learning

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Approaching (Almost) Any Machine Learning Problem

Author : Abhishek Thakur
Publisher : Abhishek Thakur
ISBN 13 : 8269211508
Total Pages : 300 pages
Book Rating : 4.2/5 (692 download)

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Book Synopsis Approaching (Almost) Any Machine Learning Problem by : Abhishek Thakur

Download or read book Approaching (Almost) Any Machine Learning Problem written by Abhishek Thakur and published by Abhishek Thakur. This book was released on 2020-07-04 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is not a traditional book. The book has a lot of code. If you don't like the code first approach do not buy this book. Making code available on Github is not an option. This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along. Table of contents: - Setting up your working environment - Supervised vs unsupervised learning - Cross-validation - Evaluation metrics - Arranging machine learning projects - Approaching categorical variables - Feature engineering - Feature selection - Hyperparameter optimization - Approaching image classification & segmentation - Approaching text classification/regression - Approaching ensembling and stacking - Approaching reproducible code & model serving There are no sub-headings. Important terms are written in bold. I will be answering all your queries related to the book and will be making YouTube tutorials to cover what has not been discussed in the book. To ask questions/doubts, visit this link: https://bit.ly/aamlquestions And Subscribe to my youtube channel: https://bit.ly/abhitubesub

Predicting movie ratings and recommender systems

Author : Arkadiusz Paterek
Publisher : Arkadiusz Paterek
ISBN 13 :
Total Pages : 196 pages
Book Rating : 4./5 ( download)

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Book Synopsis Predicting movie ratings and recommender systems by : Arkadiusz Paterek

Download or read book Predicting movie ratings and recommender systems written by Arkadiusz Paterek and published by Arkadiusz Paterek. This book was released on 2012-06-19 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: A 195-page monograph by a top-1% Netflix Prize contestant. Learn about the famous machine learning competition. Improve your machine learning skills. Learn how to build recommender systems. What's inside:introduction to predictive modeling,a comprehensive summary of the Netflix Prize, the most known machine learning competition, with a $1M prize,detailed description of a top-50 Netflix Prize solution predicting movie ratings,summary of the most important methods published - RMSE's from different papers listed and grouped in one place,detailed analysis of matrix factorizations / regularized SVD,how to interpret the factorization results - new, most informative movie genres,how to adapt the algorithms developed for the Netflix Prize to calculate good quality personalized recommendations,dealing with the cold-start: simple content-based augmentation,description of two rating-based recommender systems,commentary on everything: novel and unique insights, know-how from over 9 years of practicing and analysing predictive modeling.

Collaborative Filtering Recommender Systems

Author : Michael D. Ekstrand
Publisher : Now Publishers Inc
ISBN 13 : 1601984421
Total Pages : 104 pages
Book Rating : 4.6/5 (19 download)

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Book Synopsis Collaborative Filtering Recommender Systems by : Michael D. Ekstrand

Download or read book Collaborative Filtering Recommender Systems written by Michael D. Ekstrand and published by Now Publishers Inc. This book was released on 2011 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collaborative Filtering Recommender Systems discusses a wide variety of the recommender choices available and their implications, providing both practitioners and researchers with an introduction to the important issues underlying recommenders and current best practices for addressing these issues.

2021 3rd International Congress on Human Computer Interaction, Optimization and Robotic Applications (HORA)

Author : IEEE Staff
Publisher :
ISBN 13 : 9781665411653
Total Pages : pages
Book Rating : 4.4/5 (116 download)

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Book Synopsis 2021 3rd International Congress on Human Computer Interaction, Optimization and Robotic Applications (HORA) by : IEEE Staff

Download or read book 2021 3rd International Congress on Human Computer Interaction, Optimization and Robotic Applications (HORA) written by IEEE Staff and published by . This book was released on 2021-06-11 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The congress aims to bring scientists, experts, instructors, non govermental organizations and private sector representatives together to share and discuss theoretical and practical knowledge in a scientific framework In addition to cutting edge research paper presentations in human computer interaction, optimization and robotics areas, the congress serves as a multi disciplinary platform for discussing current issues in the engineering areas

Proceedings of the Fifth SIAM International Conference on Data Mining

Author : Hillol Kargupta
Publisher : SIAM
ISBN 13 : 9780898715934
Total Pages : 670 pages
Book Rating : 4.7/5 (159 download)

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Book Synopsis Proceedings of the Fifth SIAM International Conference on Data Mining by : Hillol Kargupta

Download or read book Proceedings of the Fifth SIAM International Conference on Data Mining written by Hillol Kargupta and published by SIAM. This book was released on 2005-04-01 with total page 670 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Fifth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. Advances in information technology and data collection methods have led to the availability of large data sets in commercial enterprises and in a wide variety of scientific and engineering disciplines. The field of data mining draws upon extensive work in areas such as statistics, machine learning, pattern recognition, databases, and high performance computing to discover interesting and previously unknown information in data. This conference results in data mining, including applications, algorithms, software, and systems.

Collaborative Filtering [microform] : a Machine Learning Perspective

Author : Benjamin Marlin
Publisher : National Library of Canada = Bibliothèque nationale du Canada
ISBN 13 : 9780612913189
Total Pages : 250 pages
Book Rating : 4.9/5 (131 download)

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Book Synopsis Collaborative Filtering [microform] : a Machine Learning Perspective by : Benjamin Marlin

Download or read book Collaborative Filtering [microform] : a Machine Learning Perspective written by Benjamin Marlin and published by National Library of Canada = Bibliothèque nationale du Canada. This book was released on 2004 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Collaborative filtering was initially proposed as a framework for filtering information based on the preferences of users, and has since been refined in many different ways. This thesis is a comprehensive study of rating-based, pure, non-sequential collaborative filtering. We analyze existing methods for the task of rating prediction from a machine learning perspective. We show that many existing methods proposed for this task are simple applications or modifications of one or more standard machine learning methods for classification, regression, clustering, dimensionality reduction, and density estimation. We introduce new prediction methods in all of these classes. We introduce a new experimental procedure for testing stronger forms of generalization than has been used previously. We implement a total of nine prediction methods, and conduct large scale prediction accuracy experiments. We show interesting new results on the relative performance of these methods.

Encyclopedia of Machine Learning

Author : Claude Sammut
Publisher : Springer Science & Business Media
ISBN 13 : 0387307680
Total Pages : 1061 pages
Book Rating : 4.3/5 (873 download)

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Book Synopsis Encyclopedia of Machine Learning by : Claude Sammut

Download or read book Encyclopedia of Machine Learning written by Claude Sammut and published by Springer Science & Business Media. This book was released on 2011-03-28 with total page 1061 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.

Recommender Systems Handbook

Author : Francesco Ricci
Publisher : Springer
ISBN 13 : 148997637X
Total Pages : 1008 pages
Book Rating : 4.4/5 (899 download)

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Book Synopsis Recommender Systems Handbook by : Francesco Ricci

Download or read book Recommender Systems Handbook written by Francesco Ricci and published by Springer. This book was released on 2015-11-17 with total page 1008 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale revision of the existing chapters, this edition includes new topics including: decision making and recommender systems, reciprocal recommender systems, recommender systems in social networks, mobile recommender systems, explanations for recommender systems, music recommender systems, cross-domain recommendations, privacy in recommender systems, and semantic-based recommender systems. This multi-disciplinary handbook involves world-wide experts from diverse fields such as artificial intelligence, human-computer interaction, information retrieval, data mining, mathematics, statistics, adaptive user interfaces, decision support systems, psychology, marketing, and consumer behavior. Theoreticians and practitioners from these fields will find this reference to be an invaluable source of ideas, methods and techniques for developing more efficient, cost-effective and accurate recommender systems.

Algorithmic Aspects in Information and Management

Author : Rudolf Fleischer
Publisher : Springer Science & Business Media
ISBN 13 : 354068865X
Total Pages : 360 pages
Book Rating : 4.5/5 (46 download)

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Book Synopsis Algorithmic Aspects in Information and Management by : Rudolf Fleischer

Download or read book Algorithmic Aspects in Information and Management written by Rudolf Fleischer and published by Springer Science & Business Media. This book was released on 2008-06-03 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th International Conference on Algorithmic Aspects in Information and Management, AAIM 2008, held in Shanghai, China, in June 2008. The 30 revised full papers presented together with abstracts of 2 invited talks were carefully reviewed and selected from 53 submissions. The papers cover original algorithmic research on immediate applications and/or fundamental problems pertinent to information management and management science. Topics addressed are: approximation algorithms, geometric data management, biological data management, graph algorithms, computational finance, mechanism design, computational game theory, network optimization, data structures, operations research, discrete optimization, online algorithms, FPT algorithms, and scheduling algorithms.

Search Engines

Author : Bruce Croft
Publisher : Pearson Higher Ed
ISBN 13 : 0133001598
Total Pages : 547 pages
Book Rating : 4.1/5 (33 download)

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Book Synopsis Search Engines by : Bruce Croft

Download or read book Search Engines written by Bruce Croft and published by Pearson Higher Ed. This book was released on 2011-11-21 with total page 547 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Search Engines: Information Retrieval in Practice is ideal for introductory information retrieval courses at the undergraduate and graduate level in computer science, information science and computer engineering departments. It is also a valuable tool for search engine and information retrieval professionals. Written by a leader in the field of information retrieval, Search Engines: Information Retrieval in Practice , is designed to give undergraduate students the understanding and tools they need to evaluate, compare and modify search engines. Coverage of the underlying IR and mathematical models reinforce key concepts. The book’s numerous programming exercises make extensive use of Galago, a Java-based open source search engine.

Programming Collective Intelligence

Author : Toby Segaran
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 0596550685
Total Pages : 361 pages
Book Rating : 4.5/5 (965 download)

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Book Synopsis Programming Collective Intelligence by : Toby Segaran

Download or read book Programming Collective Intelligence written by Toby Segaran and published by "O'Reilly Media, Inc.". This book was released on 2007-08-16 with total page 361 pages. Available in PDF, EPUB and Kindle. Book excerpt: Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect

Building a Recommendation System with R

Author : Suresh K. Gorakala
Publisher : Packt Publishing Ltd
ISBN 13 : 1783554509
Total Pages : 158 pages
Book Rating : 4.7/5 (835 download)

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Book Synopsis Building a Recommendation System with R by : Suresh K. Gorakala

Download or read book Building a Recommendation System with R written by Suresh K. Gorakala and published by Packt Publishing Ltd. This book was released on 2015-09-29 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the art of building robust and powerful recommendation engines using R About This Book Learn to exploit various data mining techniques Understand some of the most popular recommendation techniques This is a step-by-step guide full of real-world examples to help you build and optimize recommendation engines Who This Book Is For If you are a competent developer with some knowledge of machine learning and R, and want to further enhance your skills to build recommendation systems, then this book is for you. What You Will Learn Get to grips with the most important branches of recommendation Understand various data processing and data mining techniques Evaluate and optimize the recommendation algorithms Prepare and structure the data before building models Discover different recommender systems along with their implementation in R Explore various evaluation techniques used in recommender systems Get to know about recommenderlab, an R package, and understand how to optimize it to build efficient recommendation systems In Detail A recommendation system performs extensive data analysis in order to generate suggestions to its users about what might interest them. R has recently become one of the most popular programming languages for the data analysis. Its structure allows you to interactively explore the data and its modules contain the most cutting-edge techniques thanks to its wide international community. This distinctive feature of the R language makes it a preferred choice for developers who are looking to build recommendation systems. The book will help you understand how to build recommender systems using R. It starts off by explaining the basics of data mining and machine learning. Next, you will be familiarized with how to build and optimize recommender models using R. Following that, you will be given an overview of the most popular recommendation techniques. Finally, you will learn to implement all the concepts you have learned throughout the book to build a recommender system. Style and approach This is a step-by-step guide that will take you through a series of core tasks. Every task is explained in detail with the help of practical examples.

Deep Learning for Coders with fastai and PyTorch

Author : Jeremy Howard
Publisher : O'Reilly Media
ISBN 13 : 1492045497
Total Pages : 624 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Deep Learning for Coders with fastai and PyTorch by : Jeremy Howard

Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Information and Communication Theory-Source Coding Techniques-Part II

Author : Syed Thouheed Ahmed
Publisher : MileStone Research Publications
ISBN 13 : 9355784848
Total Pages : pages
Book Rating : 4.3/5 (557 download)

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Book Synopsis Information and Communication Theory-Source Coding Techniques-Part II by : Syed Thouheed Ahmed

Download or read book Information and Communication Theory-Source Coding Techniques-Part II written by Syed Thouheed Ahmed and published by MileStone Research Publications. This book was released on 2022-01-17 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook covers basic concepts of Information and mathematical theory that deals with the fundamental aspects of communication systems. The purpose of this Hand-Book is to develop the foundation ideas of information theory and to indicate where and how the theory can be applied in a real-time scenario and applications. The Handbook is categorized into two parts (PART - I & PART - II) The objectivesof this Handbook is to Explain the concepts of information source and entropy, Demonstrate the working of various Encoding Techniques, Discuss various source encoding algorithms, Illustrate the use of Cyclic and convolution codes. The readers reliability from this Handbook is to Build the basic concepts of information source and measure of information, Apply different Encoding Schemes for given applications, Develop the different Source Encoding Algorithm for given applications.

Grokking Machine Learning

Author : Luis Serrano
Publisher : Simon and Schuster
ISBN 13 : 1617295914
Total Pages : 510 pages
Book Rating : 4.6/5 (172 download)

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Book Synopsis Grokking Machine Learning by : Luis Serrano

Download or read book Grokking Machine Learning written by Luis Serrano and published by Simon and Schuster. This book was released on 2021-12-14 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you'll build interesting projects with Python, including models for spam detection and image recognition. You'll also pick up practical skills for cleaning and preparing data.

Reuse in Intelligent Systems

Author : Stuart H Rubin
Publisher : CRC Press
ISBN 13 : 1000089290
Total Pages : 253 pages
Book Rating : 4.0/5 ( download)

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Book Synopsis Reuse in Intelligent Systems by : Stuart H Rubin

Download or read book Reuse in Intelligent Systems written by Stuart H Rubin and published by CRC Press. This book was released on 2020-03-24 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is based on the best papers of IEEE IRI 2018 and IEEE FMI 2018, Salt Lake City, July, 2018. They have been enhanced and modified suitably for publication. The book comprises recent works covering several aspects of reuse in intelligent systems – including Scientific Theory and Technology-Based Applications. New data analytic algorithms, technologies, and tools are sought to be able to manage, integrate, and utilize large amounts of data despite hardware, software, and/or bandwidth constraints; to construct models yielding important data insights, and to create visualizations to aid in presenting and understanding the data. Furthermore, it addresses the representation, cleansing, generalization, validation, and reasoning strategies for the scientifically-sound and cost-effective advancement of all kinds of intelligent systems – including all software and hardware aspects. The book addresses problems such as, how to optimally select the information/data sets for reuse and how to optimize the integration of existing information/knowledge with new, developing information/knowledge sources!

Recommender System with Machine Learning and Artificial Intelligence

Author : Sachi Nandan Mohanty
Publisher : John Wiley & Sons
ISBN 13 : 1119711576
Total Pages : 448 pages
Book Rating : 4.1/5 (197 download)

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Book Synopsis Recommender System with Machine Learning and Artificial Intelligence by : Sachi Nandan Mohanty

Download or read book Recommender System with Machine Learning and Artificial Intelligence written by Sachi Nandan Mohanty and published by John Wiley & Sons. This book was released on 2020-07-08 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a multi-disciplinary effort that involves world-wide experts from diverse fields, such as artificial intelligence, human computer interaction, information technology, data mining, statistics, adaptive user interfaces, decision support systems, marketing, and consumer behavior. It comprehensively covers the topic of recommender systems, which provide personalized recommendations of items or services to the new users based on their past behavior. Recommender system methods have been adapted to diverse applications including social networking, movie recommendation, query log mining, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. Recommendations in agricultural or healthcare domains and contexts, the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. This book illustrates how this technology can support the user in decision-making, planning and purchasing processes in agricultural & healthcare sectors.