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. Next, the time series forecasting is covered in greater depth after the programme has been developed. 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. - 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 Contents1. Time Series Problems and Challenges2. Deep Learning with PyTorch 3. Time Series as Deep Learning Problem4. Recurrent Neural Networks5. Advanced Forecasting Models6. PyTorch Model Tuning with Neural Network Intelligence7. Applying Deep Learning to Real-world Forecasting Problems8. PyTorch Forecasting Package9. What is Next? Read more