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Virtual Environments
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Virtual environments isolate dependencies.
Using venv for creating environments.
Activating and deactivating virtual environments.
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Data Sources
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Essential libaries for data online data sources
Data retrieval from the CDS Toolbox
netCDF and GRIB data formats
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Data Preprocessing and Visualization
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Machine Learning Fundamentals
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Data representations are crucial for ML in science, including spatial data (vector, raster), point clouds, time series, graphs, and more
ML algorithms like linear regression, k-nearest neighbors,support vector Machine, xgboost and random forests are vital algorithms
Supervised learning is a popular ML approach, with decision trees, random forests, and neural networks being widely used
Fundamentals of data engineering are crucial for building robust ML pipelines, including data storage, processing, and serving
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Deep Learing Fundamentals
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Deep Learning algorithms are often represented as graph computation
We have different non-linear activation functions that help in learning different relationships to solve handle non-linearity in nn problems.
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Recurrent Neural Networks (RNNs) Models
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LSTMs and GRUs are advanced RNN architectures designed to handle long-term dependencies in sequential data.
The application of deep learning, particularly through RNNs and their variants like LSTM, GRU, and TFT, holds significant promise for time series forecasting in the physical sciences
LSTM, GRU and TFT models leverage advanced mechanisms for superior predictive performance in physical sciences applications.
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