Books Of India Blog

Time Series Forecasting using Deep Learning


BOOK TITLE

Time Series Forecasting using Deep Learning

SUBTITLE 
Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions (English Edition)


AUTHOR NAME
Ivan Gridin


TAGLINE 
Explore the infinite possibilities offered by Artificial Intelligence and Neural Networks
KEY FEATURES  
● Covers numerous concepts, techniques, best practices and troubleshooting tips by community experts.
● Includes practical demonstration of robust deep learning prediction models with exciting use-cases.
● Covers the use of the most powerful research toolkit such as Python, PyTorch, and Neural Network Intelligence.

DESCRIPTION 
This book is amid at teaching the readers how to apply the deep learning techniques to the time series forecasting challenges and how to build prediction models using PyTorch.


The readers will learn the fundamentals of PyTorch in the early stages of the book. Next, the time series forecasting is covered in greater depth after the programme has been developed. You will try to use machine learning to identify the patterns that can help us forecast the future results. It covers methodologies such as Recurrent Neural Network, Encoder-decoder model, and Temporal Convolutional Network, all of which are state-of-the-art neural network architectures. Furthermore, for good measure, we have also introduced the neural architecture search, which automates searching for an ideal neural network design for a certain task.


Finally by the end of the book, readers would be able to solve complex real-world prediction issues by applying the models and strategies learnt throughout the course of the book. This book also offers another great way of mastering deep learning and its various techniques.
WHAT YOU WILL LEARN
● Work with the Encoder-Decoder concept and Temporal Convolutional Network mechanics.
● Learn the basics of neural architecture search with Neural Network Intelligence.
● Combine standard statistical analysis methods with deep learning approaches.
● Automate the search for optimal predictive architecture.
● Design your custom neural network architecture for specific tasks.
● Apply predictive models to real-world problems of forecasting stock quotes, weather, and natural processes.
WHO THIS BOOK IS FOR  
This book is written for engineers, data scientists, and stock traders who want to build time series forecasting programs using deep learning. Possessing some familiarity of Python is sufficient, while a basic understanding of machine learning is desirable but not needed.
TABLE OF CONTENTS
1. Time Series Problems and Challenges
2. Deep Learning with PyTorch
3. Time Series as Deep Learning Problem
4. Recurrent Neural Networks
5. Advanced Forecasting Models
6. PyTorch Model Tuning with Neural Network Intelligence
7. Applying Deep Learning to Real-world Forecasting Problems
8. PyTorch Forecasting Package
9. What is Next?


KEYWORDS
DEEP LEARNING, TIME SERIES FORECASTING, PYTORCH,  PYTHON, PREDICTION, NEURAL NETWORKS, ARTIFICIAL INTELLIGENCE
KEYWORDS  ( 15 ) INCLUDE THE ABOVE 7 AND ADD MORE
DEEP LEARNING, TIME SERIES FORECASTING, PYTORCH, PYTHON, PREDICTION, NEURAL NETWORKS, ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, TIME SERIES, RANDOM PROCESS, TIME SERIES ANALYSIS, OPTIMIZATION, NEURAL ARCHITECTURE SEARCH, FORECASTING, TRADING
ISBN: 9789391392574
eISBN: 9789391392659
BISAC ( 3 BISAC CODES REQUIRED, please refer https://bisg.org/page/Computers )
COM004000, COM004000, COM094000, COM044000, COM051360, COM077000


COM004000 COMPUTERS / Artificial Intelligence / General
COM004000 COMPUTERS / Data Science / General
COM094000 COMPUTERS / Data Science / Machine Learning
COM044000 COMPUTERS / Data Science / Neural Networks
COM051360 COMPUTERS / Languages / Python
COM077000 COMPUTERS / Mathematical & Statistical Software

U;
MRP
INR: 899
USD: $44.50
Ebook ( 20 per cent less than INR ): 
Pages: 314
Size: 7.5*9.25 Inches
Release Date: 20-nov-2021
Binding: Paperback

AUTHOR BIO 
Ivan Gridin is a Mathematician, Fullstack Developer, Data Scientist, and Machine Learning Expert living in Moscow, Russia. Over the years, he worked on distributive high-load systems and implemented different machine learning approaches in practice. One of the key areas of his research is the design and analysis of predictive time series models.


Ivan has fundamental math skills in probability theory, random process theory, time series analysis, machine learning, deep learning, and optimization. He also has in-depth knowledge and understanding of various programming languages such as Java, Python, PHP and MATLAB.
Loving father, husband, and collector of old math books.


LinkedIn Profile: www.linkedin.com/in/survex