Many safety-critical systems, such as autonomous vehicles, rely on neural networks as state-of-the-art for image classification. Despite high accuracy, their final classification decision is difficult to verify. Uncertainty on how neural networks will behave is a challenge to safety. A known issue is providing high probabilities for unknown images. This deep dive will present some novel solutions to inspect certain parameters of trained neural network for detecting out-of-distribution data.