Combining PyTorch, RNN, TCN, and Deep Neural Network Models to Provide Production-Ready Prediction Solutions
This is the repository for Time Series Forecasting using Deep Learning, published by BPB Publications.
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.
- 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.