Machine Learning Contests: A Guidebook
(Sprache: Englisch)
This book systematically introduces the competitions in the field of algorithm and machine learning. The first author of the book has won 5 championships and 5 runner-ups in domestic and international algorithm competitions.
Firstly, it takes...
Firstly, it takes...
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Klappentext zu „Machine Learning Contests: A Guidebook “
This book systematically introduces the competitions in the field of algorithm and machine learning. The first author of the book has won 5 championships and 5 runner-ups in domestic and international algorithm competitions.Firstly, it takes common competition scenarios as a guide by giving the main processes of using machine learning to solve real-world problems, namely problem modelling, data exploration, feature engineering, model training. And then lists the main points of difficulties, general ideas with solutions in the whole process. Moreover, this book comprehensively covers several common problems in the field of machine learning competitions such as recommendation, temporal prediction, advertising, text computing, etc.The authors, also knew as "competition professionals", will explain the actual cases in detail and teach you various processes, routines, techniques and strategies, which is a rare treasure book for all competition enthusiasts. It is very suitable for readers who are interested in algorithm competitions and deep learning algorithms in practice, or computer-related majors.
Inhaltsverzeichnis zu „Machine Learning Contests: A Guidebook “
Chapter 1 First Sight.- Chapter 2 Problem Modeling.- Chapter 3 Data Exploration.- Chapter 4 Characteristic Engineering.- Chapter 5 Model Training .- Chapter 6 Model Fusion.- Chapter 7 User Portrait.- Chapter 8 Actual Combat Case: Elo Merchant.- Chapter 9 time sequence.- Chapter 10 Practical Cases: Global Urban.- Chapter 11 Practical Case: Corporaci .-Corporación Favorita Grocery Sales Forecasting.- Chapter 12 Computing Advertising.- Chapter 13 Practical Cases: Tencent 2018 Advertising Algorithm Contest-Similarity Crowd Expansion.- Chapter 14: TalkingData AdTracking Fraud Detection Challenge.- Chapter 15 Natural Language Processing.- Chapter 16 Practical Case: Quora Question Pairs.
Autoren-Porträt von Wang He, Peng Liu, Qian Qian
Wang He Currently works in Xiaomi's commercial algorithm department, engaged in the research and development of ad recommendation in app stores. He has participated in many domestic and international algorithm competitions from 2018 to 2020, and won 5 championships and 5 runner-ups, and was the champion of Tencent Advertising Algorithm Competition in 2019 and 2020. He graduated from the School of Computer Science of Wuhan University with a master's degree, and his research interest is focusing on graph data mining.Peng Liu is an algorithm engineer at Huawei Technologies Co., Ltd. and is engaged in the research and development of algorithms in the field of telecom operators and intelligent operation and maintenance. he graduated from Wuhan University in 2016 with a bachelor's degree in mathematics base class, and was admitted to the Department of Automation at the University of Science and Technology of China. His research interests during his master's degree are complex networks andmachine learning, and he has won several awards in machine learning-related competitions since 2018.
Qian Qian is the Software Algorithm Expert, working on research and development of 3d point cloud perception algorithm for Innovusion. He studied at Georgia Tech University in the U.S., and his research interests include machine learning, deep learning, natural language processing, point cloud, etc.
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