[机器学习] 机器学习 -- 分类

说明： Classification: Probabilistic Generative Model Classification Probability from Class Probability from Class - Feature Maximum Likelihood Modifying Model
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[机器学习] 机器学习 -- Deep Learning

说明： Deep Learning Three Steps for Deep Learning Neural Network Fully Connect Feedforward Network Matrix Operation Output Layer as Multi-Class Classifier
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[机器学习] 机器学习 -- Tips for Deep Learning

说明： Tips for Deep Learning Do not always blame Overfitting Hard to get the power of Deep ... Vanishing Gradient Problem ReLU Maxout RMSProp
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[机器学习] 机器学习-- ensemble

说明： Ensemble Framework of Ensemble Ensemble: Bagging Review: Bias v.s. Variance Decision Tree Experiment: Function of Miku
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[机器学习] 机器学习 -- Gradient Descent

说明： Gradient Descent Review: Gradient Descent Tip 1: Tuning your learning rates Adaptive Learning Rates Adagrad Contradiction Intuitive Reason Larger gradient, larger steps
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[机器学习] 机器学习 -- Recurrent Neural Network (RNN)

说明： Recurrent Neural Network (RNN) Example Application 1-of-N encoding Beyond 1-of-N encoding Recurrent Neural Network (RNN) Elman Network & Jordan Network Bidirectional RNN Long Short-term Memory (LSTM)
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[机器学习] 机器学习 -- keras

说明： keras Mini-batch Speed Speed - Matrix Operation Using GPU to speed training • Way 1 • THEANO_FLAGS=device=gpu0 python YourCode.py • Way 2 (in your code) • import os • os.environ["THEANO_FLAGS"] = "device=gpu0"
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[机器学习] 机器学习 -- 逻辑回归

说明： Classification: Logistic Regression Step 1: Function Set Step 2: Goodness of a Function Step 3: Find the best function Cross Entropy v.s. Square Error
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[机器学习] LSTM C++源代码

说明： https://blog.csdn.net/u012465304/article/details/82656803 我的博客上的LSTM例程，用c++写的，由于找我私聊要的人太多了，就上传到这个上面，大家可自行下载
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[机器学习] 机器学习 -- Matrix Factorization

说明： Matrix Factorization More about Matrix Factorization Matrix Factorization for Topic analysis
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[机器学习] 机器学习 -- Unsupervised Learning: Principle Component Analysis

说明： Unsupervised Learning: Principle Component Analysis Dimension Reduction Clustering Distributed Representation PCA PCA – Another Point of View
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[机器学习] 机器学习 -- 回归

说明： Regression Regression: Output a scalar Step 1: Model Step 2: Goodness of Function Step 3: Best Function
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