max tokens
├── .gitignore
├── README.md
├── bak
├── README.md
└── function-approximation
│ ├── README.md
│ └── figures
│ ├── long.png
│ └── model.png
├── pending
├── CNN_deepdream
│ ├── DeepDream.py
│ └── README.md
├── README.md
├── RNN
│ ├── README.md
│ └── RNN.py
└── 蒙特卡洛方法实现
│ ├── Gibbs.ipynb
│ ├── MCMC_based_EBM.ipynb
│ ├── MCMC_strategy.ipynb
│ ├── MNIST_RBM_Gibbs.ipynb
│ ├── Metropolis-Hastings.ipynb
│ ├── README.md
│ ├── Tempered_Transitions_MC.ipynb
│ ├── data
│ └── MNIST
│ │ └── raw
│ │ ├── t10k-images-idx3-ubyte
│ │ ├── t10k-images-idx3-ubyte.gz
│ │ ├── t10k-labels-idx1-ubyte
│ │ ├── t10k-labels-idx1-ubyte.gz
│ │ ├── train-images-idx3-ubyte
│ │ ├── train-images-idx3-ubyte.gz
│ │ ├── train-labels-idx1-ubyte
│ │ └── train-labels-idx1-ubyte.gz
│ ├── importance_sampling.ipynb
│ └── mixing_problem.ipynb
├── 卷积网络
├── GoogLeNet.ipynb
├── README.md
├── ResNet.ipynb
├── img
│ ├── C3.png
│ ├── DBA.PNG
│ ├── DBA_implemention.PNG
│ ├── Industry.png
│ ├── activation.PNG
│ ├── cifar10.PNG
│ ├── googlenet.jpg
│ ├── inception.PNG
│ ├── network-structure.png
│ ├── res_block.PNG
│ ├── resnet.PNG
│ ├── share-weight.png
│ ├── shortcut.PNG
│ ├── shortcut_res.PNG
│ ├── sparse-representation.png
│ └── training_googlenet.PNG
├── src
│ └── simple-mnist-CNN.ipynb
├── 卷积网络进阶.ipynb
└── 简单卷积网络.md
├── 实践调参
├── README.md
├── img
│ ├── 1e-3.png
│ ├── 2e-2.png
│ ├── 3e-2.png
│ ├── 4e-2.png
│ ├── 5e-3.png
│ ├── 5e-4.png
│ ├── 6e-2.png
│ ├── 7e-3.png
│ ├── grid search VS random search.png
│ └── model-graph.PNG
└── src
│ ├── Practical-Methodology.ipynb
│ └── SVHN.ipynb
├── 循环递归网络
├── CharRNN.md
├── LSTM.md
├── RNN.md
├── RNN_MNIST.ipynb
├── Sequence.md
├── data
│ ├── train_poetry.txt
│ └── train_poetry_s.txt
├── img
│ ├── char-rnn.svg
│ ├── forget.png
│ ├── input.png
│ ├── lstm_label.png
│ ├── lstm_model.png
│ ├── output.png
│ ├── rnn.png
│ ├── rnn_lstm.png
│ ├── sinx.png
│ ├── sinxx.png
│ ├── state1.png
│ ├── state2.png
│ ├── state_update.png
│ └── update.png
├── poetry-charRNN.ipynb
├── rnn_func_approx.ipynb
└── src
│ ├── RNN_MNIST.py
│ ├── data
│ └── dataset.csv
│ ├── lstm_sin2cos.py
│ └── rnn_stock.py
├── 数学基础
├── README.md
├── img
│ ├── gradient_descent_loss.png
│ ├── graph_model.png
│ ├── laplace_dist.png
│ ├── local_min.png
│ ├── local_min_1.png
│ ├── logistic_curve.png
│ ├── logistic_feature.png
│ ├── max_min_saddle_point.png
│ ├── normal_dist.png
│ ├── shannon_entropy.png
│ ├── softplus_and_relu.png
│ ├── softplus_feature.png
│ └── undirected_graph_model.png
├── src
│ └── gradient_descent_example.ipynb
├── 数值计算.md
├── 概率与信息论.md
└── 线性代数.md
├── 有向生成网络
├── DCGAN
│ ├── DCGAN_Case.py
│ ├── image_generative_begin.png
│ └── image_generative_last.png
├── GAN
│ ├── GAN_Case.py
│ ├── image_generative_begin.png
│ └── image_generative_last.png
├── README.md
├── VAE
│ ├── VAE_Case.py
│ ├── image_generative_last.png
│ └── loss_VAE.png
└── image
│ ├── GAN0.png
│ ├── ZHG0.png
│ ├── ZHG1.png
│ └── ZHG2.png
├── 机器学习基础与实践
├── README.md
├── TensorFlow实战.md
├── img
│ ├── MNIST.png
│ ├── bias_variance.png
│ ├── bias_variance_error.png
│ ├── confusionmatrix.png
│ ├── mse.png
│ ├── overfitting.png
│ ├── simple_graph_example.png
│ ├── tensor_variable.png
│ ├── tensorflow-graph-example.png
│ └── x_1_expand.png
├── src
│ └── linear_model.ipynb
└── 机器学习基础.md
├── 深度前馈网络
├── README.md
├── img
│ ├── 3_1_2_optimization_problem_1.png
│ ├── 3_1_2_optimization_problem_2.png
│ ├── 3_1_2_optimization_problem_3.png
│ ├── chain_rule.png
│ ├── eh_equation.png
│ ├── maximum_likelihood.png
│ ├── maxout.png
│ ├── mean_standard_error.png
│ ├── network_archi.png
│ ├── origin_space_and_learned_space.png
│ ├── simple_mpl.png
│ ├── softmax.png
│ ├── softmax_cost.png
│ ├── training_set.png
│ └── xor_result.jpg
├── src
│ ├── XOR.md
│ ├── XOR.py
│ └── XORV2.py
└── 深度前馈网络.md
├── 深度学习中的优化
├── README.md
├── img
│ ├── Saddle.png
│ ├── algorithm8.1.png
│ ├── algorithm8.2.png
│ ├── algorithm8.3.png
│ ├── algorithm8.4.png
│ ├── algorithm8.5.png
│ ├── algorithm8.6.png
│ ├── algorithm8.7.png
│ ├── algorithm8.8.png
│ ├── algorithm8.9.png
│ ├── gradientCliff.png
│ ├── local_all.png
│ ├── lr.png
│ ├── mlp.png
│ ├── opt1.gif
│ ├── opt2.gif
│ ├── relation.png
│ └── sgd_momentum.png
├── src
│ └── SGD_optimizatioin_demo.ipynb
└── 深度学习中的优化.md
├── 深度学习中的正则化
├── README.md
├── img
│ ├── Dropout.png
│ ├── L1_regularization.png
│ ├── L2_regularization.png
│ ├── multi_task_learning.png
│ └── sparse_representation.png
└── 深度学习中的正则化.md
├── 玻尔兹曼机
├── DBM示意图.png
├── DBN示意图.png
├── RBM示意图.png
├── README.md
└── 玻尔兹曼机.ipynb
├── 生成对抗网络
├── README.md
├── gaussian_gan_pytorch.py
├── img
│ ├── final03.jpg
│ ├── final19.jpg
│ ├── gaussian.png
│ ├── gaussian_gan.png
│ ├── mnist_epoch_1.png
│ ├── mnist_epoch_25.png
│ ├── mnist_epoch_50.png
│ └── training.gif
├── mnist_gan.py
├── mnist_gan_pytorch.py
├── simple_gan.ipynb
└── simple_gan_pytorch.ipynb
├── 线性因子模型
├── ICA.ipynb
├── PCA.md
├── PPCA.md
├── README.md
├── data_sound
│ ├── mix1.wav
│ ├── mix2.wav
│ ├── my_ica1.wav
│ └── my_ica2.wav
└── img
│ ├── image.png
│ ├── image2.png
│ ├── image3.png
│ └── image4.png
├── 结构化概率模型
├── README.md
├── code
│ └── 概率模型的内存开销.py
└── note
│ ├── 16.结构化概率模型.md
│ ├── image-1.png
│ ├── image-2.png
│ ├── image-3.png
│ ├── image-4.png
│ ├── image-5.png
│ └── image.png
├── 自编码器
├── AutoEncoder.md
├── README.md
└── img
│ ├── image.png
│ ├── image2.png
│ ├── image3.png
│ ├── image4.png
│ ├── image5.png
│ ├── image6.png
│ └── image7.png
├── 蒙特卡洛方法
└── README.md
└── 表示学习
├── README.md
├── code
├── GAN.py
├── MNIST_CNN.py
└── MNIST_Variational_AutoEncoder.py
└── note
└── 15.表示学习.md
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