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A five-course specialization covering the foundations of Deep Learning, from building CNNs, RNNs & LSTMs to choosing model configurations & paramaters like Adam, Dropout, BatchNorm, Xavier/He initialization, and others.
Analyze a given series with useful calculations, particularly the nth partial sum. Integrates well with data analysis and visualization libraries in Python.
As part of this project, I have developed algorithms from scratch using Gradient Descent method. The first algorithm developed will be used to predict the average GPU Run Time and the second algorithm will be used to classify a GPU run process as high or low time consuming process.
Provides a c++ code to do a fast MCMC search with 4 params quickly. Performs trilinear interpolation saves the chains and check for convergence. However, use has to provide the4d grid and grid of models.