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How to choose it and minimize your neural network training time. Image source: unsplash.com. Developing any machine learning model involves a rigorous experimental process that follows the idea-experiment-evaluation cycle. Image by the author. The above cycle is repeated multiple times until satisfactory performance levels are achieved. The "experiment" phase involves both the coding and the training steps of the machine learning model. As models become more complex and are trained over much larger datasets, training time inevitably expands. As a consequence, training a large deep neural network can be painfully slow. Fortunately for data science practitioners, there exist several techniques to accelerate the training process, including: Transfer Learning. Weight Initialization, as Glorot or He initialization. Batch Normalization for training data. Picking a reliable activation function. Use a faster optimizer. While all the techniques I pointed out are

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