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从无序到正态分布：信息熵的极值原理
本篇从信息量的基本性质出发，导出信息熵的定义，并借助统计力学中的热力学熵给出其直观诠释。在此基础上，我们将证明，在均值与方差给定的约束下，使信息熵取得最大值的概率分布正是正态分布。
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Word Embeddings (Deep Learning Notes C5W2)
generation of dense vector representations of words that can capture semantic relationships between words
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从概率到预测：理解最大似然估计与线性回归
笔记将以正态分布为例，推导最大似然估计的求解过程，分析其统计偏差，并展示其与线性回归中误差平方和最小化之间的联系
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LSTM and GRU Networks (Deep Learning Notes C5W1B)
introduction to LSTM and GRU networks: forward pass and backward pass
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Recurrent Neural Networks (Deep Learning Notes C5W1A)
basics of RNNs: features and problems; forward propagation and backward pass
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Neural Style Transfer (Deep Learning Notes C4W4)
generation of an image that blends the content from one image and the style of the other
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Face Recognition (Deep Learning Notes C4W4)
training a Siamese network for face recognition tasks with a triplet loss function
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Image Semantic Segmentation (Deep Learning Notes C4W3)
labelling and colouring the pixels of an image into a set of predefined classes