Time Series Forecasting using Deep Learning: Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions (eBook, ePUB)
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Explore the infinite possibilities offered by Artificial Intelligence and Neural NetworksKEY 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.DESCRIPTIONThis 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 mo...
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.
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.
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.
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.
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