他用一根橡皮筋，讲透了AI的底层逻辑

Core Viewpoint - The article discusses the mechanisms of deep learning, particularly focusing on the concepts of forward propagation and backward propagation, using a metaphor of a large company to explain how neural networks function and learn from data [5][16][50]. Group 1: Forward Propagation - The neural network is likened to a company with 1 billion employees, where each layer of neurons corresponds to different levels of responsibility in recognizing patterns in images [18][21]. - The first layer detects basic pixel information, while subsequent layers identify more complex features, culminating in a final decision made by the top layer [22][24][26]. - The initial weights assigned to connections between neurons are random, leading to potentially incorrect predictions during the first pass of data [24][26]. Group 2: Error and Gradient - When the network makes a wrong prediction, it calculates the error (loss) by comparing its output to the true label, which is represented by a "truth pin" on a scale [28][30]. - The distance between the predicted output and the true label creates tension, represented by a rubber band, which indicates the magnitude of the error [30][32]. - The gradient, or direction of change needed to reduce the error, is derived from this tension, guiding the adjustments in the network's weights [33][37]. Group 3: Backward Propagation - To alleviate the tension caused by errors, the network employs backward propagation, where the CEO (top neuron) distributes the pain of the error down through the layers [41][45]. - Each layer adjusts its weights based on the gradient received from the layer above, effectively redistributing the responsibility for the error [41][45]. - This process continues down to the input layer, ensuring that all neurons receive feedback on their contributions to the error, leading to a refined learning process [49][50]. Group 4: Emergence of Intelligence - After multiple iterations of training with numerous images, the network fine-tunes its weights, leading to a significant improvement in its ability to recognize patterns accurately [53][56]. - The culmination of this training results in a model that can make precise predictions, with the error tension fully relaxed, indicating a well-trained neural network [58][60].