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Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Jason Brownlee | - |
| dc.date.accessioned | 2026-04-23T07:16:30Z | - |
| dc.date.available | 2026-04-23T07:16:30Z | - |
| dc.date.issued | 2018 | - |
| dc.identifier.uri | http://thuvienso.thanglong.edu.vn//handle/TLU/13688 | - |
| dc.description.abstract | Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. With clear explanations, standard Python libraries, and step-by-step tutorial lessons you’ll discover how to develop deep learning models for your own time series forecasting projects. | vi |
| dc.language.iso | en | vi |
| dc.publisher | Machine Learning Mastery | vi |
| dc.subject | Deep learning (Machine learning) | Neural networks (Computer science) | Python (Computer program language) | Dự báo chuỗi thời gian | Học sâu | vi |
| dc.title | Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python | vi |
| dc.type | Sách/Book | vi |
| Appears in Collections | Khoa học máy tính - Toán | |
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