文件名称：ML-Scikit-Keras-TensorFlow：AurélienGéron撰写的“使用Scikit-Learn，Keras和TensorFlow进行动手机器学习”的注释和代码
文件大小：9.82MB
文件格式：ZIP
更新时间：2024-02-24 19:19:03
machine-learning tensorflow scikit-learn keras scikit-learnJupyterNotebook
ML-Scikit-Keras-TensorFlow：AurélienGéron撰写的“使用Scikit-Learn，Keras和TensorFlow进行动手机器学习”的注释和代码
【文件预览】：
ML-Scikit-Keras-TensorFlow-master
----.ipynb_checkpoints()
--------09.Unsupervised_Learning-checkpoint.ipynb(94KB)
--------05_Support_Vector_Machines-checkpoint.ipynb(35KB)
--------1.Machine_Learning_Landscape-checkpoint.ipynb(4KB)
--------14.Deep_Computer_Vision_Using_Convolutional_Neural_Networks-checkpoint.ipynb(19KB)
--------08.Dimensionality_Reduction-checkpoint.ipynb(11KB)
--------07.Ensemble_Learning_and_Random_Forests-checkpoint.ipynb(11KB)
--------11.Training_Deep_Neural_Networks-checkpoint.ipynb(33KB)
--------17.Representation_Learning_and_Generative_Learning_Autoencoders_GANs-checkpoint.ipynb(21KB)
--------03.Classification-checkpoint.ipynb(109KB)
--------12.Custom_Models_and_Training_with_TensorFlow-checkpoint.ipynb(44KB)
--------06.Decision_Trees-checkpoint.ipynb(6KB)
--------15.Processing_Sequences_Using_RNNs_CNNs-checkpoint.ipynb(14KB)
--------13. Loading_and_Preprocessing_Data_with_TensorFlow-checkpoint.ipynb(30KB)
--------19.Training_and_Deploying_TensorFlow_Models_Scale-checkpoint.ipynb(9KB)
--------01.Machine_Learning_Landscape-checkpoint.ipynb(4KB)
--------16.Natural_Language_Processing_RNNs_and_Attention-checkpoint.ipynb(23KB)
--------02.Machine_Learning_Project-checkpoint.ipynb(64KB)
--------18.Reinforcement_Learning-checkpoint.ipynb(27KB)
--------04.Training_Models-checkpoint.ipynb(38KB)
--------10.Introduction_Artificial_Neural_Networks_Keras-checkpoint.ipynb(66KB)
----10.Introduction_Artificial_Neural_Networks_Keras.ipynb(428KB)
----07.Ensemble_Learning_and_Random_Forests.ipynb(11KB)
----19.Training_and_Deploying_TensorFlow_Models_Scale.ipynb(9KB)
----15.Processing_Sequences_Using_RNNs_CNNs.ipynb(14KB)
----my_data.tfrecord(85B)
----.gitattributes(66B)
----14.Deep_Computer_Vision_Using_Convolutional_Neural_Networks.ipynb(19KB)
----models()
--------forest_reg.pkl(10.67MB)
----17.Representation_Learning_and_Generative_Learning_Autoencoders_GANs.ipynb(21KB)
----forest_reg.pkl(12.2MB)
----06.Decision_Trees.ipynb(6KB)
----13. Loading_and_Preprocessing_Data_with_TensorFlow.ipynb(30KB)
----images()
--------4.lin_reg_algos_comparison.JPG(41KB)
--------5.SVM_Classification.JPG(76KB)
--------6.Iris_Decision_Tree.JPG(95KB)
----my_mnist_model()
--------0001()
----datasets()
--------housing()
--------shakespeare()
----02.Machine_Learning_Project.ipynb(64KB)
----Images()
--------15.RNN.PNG(138KB)
--------15.LSTM.PNG(172KB)
--------10.ANN.PNG(266KB)
--------10.TLU.PNG(254KB)
--------1.1.Test_accuracy_vs_Dataset_size.JPG(31KB)
--------11.ELU.PNG(34KB)
--------16.Encoder-Decoder.PNG(267KB)
--------17.Autoencoders_Pretraining.PNG(179KB)
--------16.Transformer.PNG(145KB)
--------9.1_BGMM.png(131KB)
--------16.Encoder-Decoder_Attention.PNG(254KB)
--------13.CNN_Layers.PNG(332KB)
--------9.1_GMM.PNG(81KB)
--------11.Optimizers.PNG(33KB)
--------11.Leaky_ReLU.PNG(35KB)
----LICENSE(1KB)
----05_Support_Vector_Machines.ipynb(41KB)
----04.Training_Models.ipynb(38KB)
----README.md(396B)
----11.Training_Deep_Neural_Networks.ipynb(33KB)
----16.Natural_Language_Processing_RNNs_and_Attention.ipynb(22KB)
----09.Unsupervised_Learning.ipynb(94KB)
----12.Custom_Models_and_Training_with_TensorFlow.ipynb(44KB)
----.gitignore(70B)
----01.Machine_Learning_Landscape.ipynb(4KB)
----03.Classification.ipynb(109KB)
----18.Reinforcement_Learning.ipynb(27KB)
----08.Dimensionality_Reduction.ipynb(11KB)