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Ivan Gridin - Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions

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Ivan Gridin Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions
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    Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions
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Ivan Gridin - Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions
Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions
Ivan Gridin
Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions
Ivan Gridin - Learning Genetic Algorithms with Python: Empower the performance of Machine Learning and AI models with the capabilities of a powerful search algorithm (English Edition)
Learning Genetic Algorithms with Python: Empower the performance of Machine Learning and AI models with the capabilities of a powerful search algorithm (English Edition)
Ivan Gridin
Learning Genetic Algorithms with Python: Empower the performance of Machine Learning and AI models with the capabilities of a powerful search algorithm (English Edition)

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Table of Contents
Guide
Time Series Forecasting using Deep Learning - photo 1
Time Series
Forecasting using
Deep Learning
Combining PyTorch RNN TCN and Deep Neural Network Models to Provide - photo 2
Combining PyTorch, RNN, TCN, and
Deep Neural Network Models to Provide
Production-Ready Prediction Solutions
Time Series Forecasting using Deep Learning Combining PyTorch RNN TCN and Deep Neural Network Models to Provide Production-Ready Prediction Solutions - image 3
Ivan Gridin
Time Series Forecasting using Deep Learning Combining PyTorch RNN TCN and Deep Neural Network Models to Provide Production-Ready Prediction Solutions - image 4www.bpbonline.comFIRST EDITION 2022Copyright BPB Publications, IndiaISBN: 978-93-91392-574All Rights Reserved. No part of this publication may be reproduced, distributed or transmitted in any form or by any means or stored in a database or retrieval system, without the prior written permission of the publisher with the exception to the program listings which may be entered, stored and executed in a computer system, but they can not be reproduced by the means of publication, photocopy, recording, or by any electronic and mechanical means.LIMITS OF LIABILITY AND DISCLAIMER OF WARRANTYThe information contained in this book is true to correct and the best of authors and publishers knowledge. The author has made every effort to ensure the accuracy of these publications, but publisher cannot be held responsible for any loss or damage arising from any information in this book.All trademarks referred to in the book are acknowledged as properties of their respective owners but BPB Publications cannot guarantee the accuracy of this information.wwwbpbonlinecom It is useless to teach you something You cant do - photo 5www.bpbonline.comIt is useless to teach you something! You can't do anything with your hands! Please choose a profession in which you do not need to work with your hands. Maybe just typing, and that's itAleksandr Gridin
1960-2007
About the Author
Ivan Gridinis a Mathematician, Fullstack Developer, Data Scientist, and Machine Learning Expert living in Moscow, Russia. Over the years, he has worked on distributive high-load systems and implemented different machine learning approaches in practice. He also has in-depth knowledge and understanding of various programming languages such as Java, Python, PHP, and MATLAB.He is a loving father, husband, and collector of old math books!
About the Reviewer
Satyajeet Dhawaleis a professional Data Scientist having strong experience in machine learning, deep learning, computer vision, inferential and descriptive statistical analysis. He has worked on many projects that involve complex machine learning and deep learning algorithms and used various data sets from different domains. In his career, he has successfully delivered many machine learning and deep learning solutions for complex data problems. You can find more professional details about Satyajeet on LinkedIn. ( https://www.linkedin.com/in/satyajeet-dhawale/)
Acknowledgement
There are a few people I want to thank for the idea and the motivation for writing this book. I thank my adorable wife Tamara; her patience and beauty inspired me every day. I thank my elder daughter Ksenia; her courage and determination motivated me in exhaustion moments. And my little daughter Elena for waking me up earlier you're my energizer!I am endlessly grateful to my company AT Consulting. This company has done a lot to make me the expert I am. I especially thank my colleague Alexey Korotaev - one of the best managers I've ever seen. Also, I want to thank the head of my department, Dmitrii Sagalaev for giving me a chance to work on this book. I'm proud to be a part of this big team.Thanks to my friends, who helped me in all my efforts. I want especially thank Alisher Alimov for his valuable help when I started to make the first steps in programming, Igor Zuykov and Denis Vasin for their help in learning programming and mathematics, Evgenii Sushinskii for his valuable tutoring in machine learning, and Evgenii Sokolov for the inspiration and fun that he brings into my life.
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