hands-on machine learning with scikit-learn and tensorflow代码答案

【实例简介】
hands-on machine learning with scikit-learn and tensorflow代码答案
【实例截图】
【核心代码】
handson-ml-master
└── handson-ml-master
├── 01_the_machine_learning_landscape.ipynb
├── 02_end_to_end_machine_learning_project.ipynb
├── 03_classification.ipynb
├── 04_training_linear_models.ipynb
├── 05_support_vector_machines.ipynb
├── 06_decision_trees.ipynb
├── 07_ensemble_learning_and_random_forests.ipynb
├── 08_dimensionality_reduction.ipynb
├── 09_up_and_running_with_tensorflow.ipynb
├── 10_introduction_to_artificial_neural_networks.ipynb
├── 11_deep_learning.ipynb
├── 12_distributed_tensorflow.ipynb
├── 13_convolutional_neural_networks.ipynb
├── 14_recurrent_neural_networks.ipynb
├── 15_autoencoders.ipynb
├── 16_reinforcement_learning.ipynb
├── book_equations.ipynb
├── datasets
│   ├── housing
│   │   ├── housing.csv
│   │   ├── housing.tgz
│   │   └── README.md
│   ├── inception
│   │   └── imagenet_class_names.txt
│   └── lifesat
│   ├── gdp_per_capita.csv
│   ├── oecd_bli_2015.csv
│   └── README.md
├── images
│   ├── ann
│   │   └── README
│   ├── autoencoders
│   │   └── README
│   ├── classification
│   │   └── README
│   ├── cnn
│   │   ├── README
│   │   └── test_image.png
│   ├── decision_trees
│   │   └── README
│   ├── deep
│   │   └── README
│   ├── dim_reduction
│   │   └── README
│   ├── distributed
│   │   └── README
│   ├── end_to_end_project
│   │   ├── california.png
│   │   └── README
│   ├── ensembles
│   │   └── README
│   ├── fundamentals
│   │   └── README
│   ├── rl
│   │   └── README
│   ├── rnn
│   │   └── README
│   ├── svm
│   │   └── README
│   ├── tensorflow
│   │   └── README
│   └── training_linear_models
│   └── README
├── index.ipynb
├── LICENSE
├── math_linear_algebra.ipynb
├── README.md
├── requirements.txt
├── tools_matplotlib.ipynb
├── tools_numpy.ipynb
└── tools_pandas.ipynb

22 directories, 50 files