Python Devs Visualize ReLU Activation, Discover Deep Learning Is Just max(0, *Hand Wave*) With Graphs

A recent article explored the Rectified Linear Unit (ReLU) activation function, a crucial component in deep learning and convolutional neural networks. ReLU is defined as ReLU(x) = max(0, x), with an output range from 0 to infinity. Using Python examples, the article demonstrated how ReLU works with dosage values from 0 to 1, resulting in a bent blue line. The curve was then scaled and flipped downward by multiplying it with -40.8, and a straight orange line was generated using w2 and b2. The two lines were combined, resulting in a green wedge-shaped curve, which was then adjusted with a bias of -16. Finally, ReLU was applied to the combined signal, converting negative values to 0 and keeping positive values unchanged. The article provided a comprehensive understanding of ReLU, making it more realistic for real-world situations, and will be followed by further explorations of neural networks in upcoming articles.