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13min chapter
Becoming a deep learning researcher without a PhD, graph neural network(GNN), time series, recommender system with Kyle Kranen - The Data Scientist Show#028
The Data Scientist Show
Exploring Model Performance and Cross-Domain Applicability
The chapter compares the performance of TFT with other models like ARIMA and LSTM, emphasizing TFT's superiority in handling non-stationary time series and diverse data distributions. It also discusses the benefits of borrowing techniques across domains and the importance of exploring various research areas for enhancing machine learning architectures. Furthermore, the conversation touches on the differences between Prophet and TFT models, focusing on human intuition integration and feature handling approaches.
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