Let's Get Activated! Why Non-Linear Activation Matters

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This research paper explores the impact of different activation functions, specifically ReLU and L-ReLU, on the performance of deep learning models. The authors investigate how the choice of activation function, along with factors like the number of parameters and the shape of the model architecture, influence model accuracy across various data domains (continuous, categorical with and without transfer learning). The study concludes that L-ReLU is more effective than ReLU when the number of parameters is relatively small, while ReLU generally performs better with larger models. The paper also highlights the importance of considering the specific data domain and the use of pre-trained models for transfer learning when selecting the most suitable activation function.

Read more: https://github.com/christianversloot/machine-learning-articles/blob/main/why-nonlinear-activation-functions-improve-ml-performance-with-tensorflow-example.md