ML摘要

Machine Learning

1.Embeddings

1.An embedding is a mapping from discrete objects, such as words, to vectors of real numbers. For example, a 300-dimensional embedding for English words could include:

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blue:  (0.01359, 0.00075997, 0.24608, ..., -0.2524, 1.0048, 0.06259)
blues:  (0.01396, 0.11887, -0.48963, ..., 0.033483, -0.10007, 0.1158)
orange:  (-0.24776, -0.12359, 0.20986, ..., 0.079717, 0.23865, -0.014213)
oranges:  (-0.35609, 0.21854, 0.080944, ..., -0.35413, 0.38511, -0.070976)

2.Is an embedding the same as an embedding layer?
No. An embedding layer is a part of neural network, but an embedding is a more general concept.

2.NFM与AFM用于CTR预估

  • 网络结构，通常，钻石型的网络结构往往优于其他结构
  • 隐藏层单元数不是越高越好，中间有一个临界值达到最优
  • Dropout在数据量本来就很稀疏的情况下尽量不用，不同的数据集dropout表现差距比较大