├── .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 │ ├── 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