PinnedDr. Sandeep Singh Sandha, PhDTuning Parameters of Prophet for Forecasting: An Easy Approach in PythonComplete_Github_Notebook_to_Play. The tuned model gives optimal results on unseen future predictions.Jan 2, 20233Jan 2, 20233
PinnedDr. Sandeep Singh Sandha, PhDParallel Hyperparameter Tuning in Python: An IntroductionThis post assumes introductory experience in machine learning pipelines. However, the concepts are explained without touching unnecessary…Dec 29, 20221Dec 29, 20221
Dr. Sandeep Singh Sandha, PhDFine-Tuning Deep Learning Models with Low-Rank Adaptation (LORA): using Google ColabWelcome to an exciting journey through the world of model fine-tuning using Low-Rank Adaptation (LORA), a novel technique designed for the…Feb 25Feb 25
Dr. Sandeep Singh Sandha, PhDDeep-Learning for Time Series Forecasting: LSTM and CNN NeurToday, we will use a very simple deep-learning architecture that often gives state-of-the-art results. This model has only ~700 parameters…Jan 3, 20232Jan 3, 20232
Dr. Sandeep Singh Sandha, PhDXGBoost for Time Series Extrapolation: An Approach in PythonXGboost represents a very powerful class of classical models. However, if the time series has trends, XGBoost cannot extrapolate it. We…Jan 1, 20231Jan 1, 20231
Dr. Sandeep Singh Sandha, PhDTuning ARIMA for Forecasting: An Easy Approach in PythonVideo for this blog: https://www.youtube.com/watch?v=Lg-5ooU5Xs8Dec 31, 20221Dec 31, 20221