Aug

26

2022

Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production

BaDshaH 26 Aug 2022 06:28 LEARNING » e-book


Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production

English | 2021 | ISBN: 9391392571 | 314 pages | True EPUB | 6.27 MB

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 aims to teach the readers how to apply 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 program has been developed. You will try to use machine learning to identify the patterns that can help us forecast 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 learned throughout 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.

Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production

Download From Rapidgator


https://rapidgator.net/file/101bd88abc97212613b7146c31c43ed6


Download From Nitroflare


https://nitroflare.com/view/3A58F42AFA8B520


Download From 1DL


https://1dl.net/vcfd6zp3ji2h



To Support My Work Buy Premium From My Links.

High Speed Download

Add Comment

  • People and smileys emojis
    Animals and nature emojis
    Food and drinks emojis
    Activities emojis
    Travelling and places emojis
    Objects emojis
    Symbols emojis
    Flags emojis