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目录 Outline Auto-Encoder 创建编解码器 训练 Outline Auto-Encoder Variational Auto-Encoders Auto-Encoder 创建编解码器 import os import tensorflow as tf import numpy as 阅读全文
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目录AE v.s. VAEGenerative modelVAE v.s. GAN AE v.s. VAE Generative model VAE v.s. GAN 阅读全文
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目录Sample() is not differentiableReparameterization trickToo Complex Sample() is not differentiable 现在我们得到的不是一个向量，得到的是一个分布，而分布是无法使用梯度下降的 Reparameteriza 阅读全文
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目录Another Approach: q(z)->p(z)Intuitively comprehend KL(p|q)Minimize KL DivergenceHow to compute KL between q(z) and p(z) Distribution of hidden code 阅读全文
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目录PCA V.S. Auto-EncodersDenoising AutoEncodersDropout AutoEncoders PCA V.S. Auto-Encoders deep autoencoder由深度神经网络构成，因此降维效果丢失数据少 左pca；右auto-encoder Den 阅读全文
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目录Auto-EncodersHow to Train? Auto-Encoders How to Train? 阅读全文
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目录Supervised LearningMassive Unlabeled dataUnsupervised LearningWhy needed Supervised Learning Massive Unlabeled data Unsupervised Learning Why needed 阅读全文
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目录 Sentiment Analysis Two approaches Single layer Multi-layers Sentiment Analysis Two approaches SimpleRNNCell single layer multi-layers RNNCell Singl 阅读全文
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目录Recapinput dim, hidden dimSimpleRNNCellSingle layer RNN CellMulti-Layers RNNRNN Layer Recap input dim, hidden dim from tensorflow.keras import layer 阅读全文
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目录RecapSentiment AnalysisProposalS1.Weight sharingNaive versionWeight shareS2.Consistent memoryUnfolded modelFormulationOverall DiagramOne more thingH 阅读全文