Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions (English Edition)

Gridin, Ivan

ISBN 10: 9391392571 / ISBN 13: 9789391392574

Editore: BPB Publications, 2021

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Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

Codice articolo M09391392571-V

Dati bibliografici

• Titolo: Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions (English Edition)
• Editore: BPB Publications
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 9391392571
• ISBN 13: 9789391392574
• Rilegatura: Brossura
• Condizione: very good

Descrizione articolo

Riassunto

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?