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本文介绍了深度学习的基础，包括人工神经网络的构成、不同类型的激活函数（如Sigmoid、ReLU等）、损失函数（如MAE、MSE等）以及优化器（如梯度下降、Adam等）。此外，还讨论了神经网络的训练过程，如批量梯度下降，并提及了多层感知器和反向传播算法。最后，提到了学习率调度策略，如幂调度和指数衰减。
Deep learning represents the very cutting edge of Artificial Intelligence (AI). Unlike machine learning, deep learning takes a different approach in making predictions by using a neural network. An artificial neural network is modeled on the human nervous system, consisting of an input layer and an output layer, with one or more hidden layers in between. Each layer consists of artificial neurons working in parallel and passing outputs to the next layer as inputs. The word deep in deep learning comes from the notion that as data passes through more hidden layers in an artificial neural network, more complex features can be extracted.
TensorFlow is an open source, powerful machine learning
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