文件名称：Jason-Brownlee-deep_learning_with_python(pdf + sourcecode v1.15 2018).zip
文件大小：6.93MB
文件格式：ZIP
更新时间：2022-09-25 15:29:31
Keras Tensorflow Deep Learning
Deep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Tap into their power in a few lines of code using Keras, the best-of-breed applied deep learning library. In this mega Ebook is written in the friendly Machine Learning Mastery style that you’re used to, learn exactly how to get started and apply deep learning to your own machine learning projects. After purchasing you will get: 256 Page PDF Ebook. 66 Python Recipes. 18 Step-by-Step Lessons. 9 End-to-End Projects. 参看 https://machinelearningmastery.com/deep-learning-with-python/ 价值 $47 USD
【文件预览】：
README.txt
code
----chapter_22()
--------imdb_plot.py(819B)
--------imdb_mlp.py(1KB)
--------imdb_cnn.py(1KB)
----chapter_10()
--------iris.csv(4KB)
--------iris_example.py(1KB)
----chapter_25()
--------lstm_stacked.py(3KB)
--------lstm_stateful.py(3KB)
--------international-airline-passengers.csv(2KB)
--------lstm_window.py(3KB)
--------lstm_simple.py(3KB)
--------lstm_time_steps.py(3KB)
----chapter_20()
--------augment_feature_standardize.py(1KB)
--------augment_baseline.py(344B)
--------augment_zca.py(967B)
--------augment_shifts.py(1009B)
--------augment_save_to_file.py(1KB)
--------augment_rotations.py(968B)
--------augment_flips.py(987B)
----chapter_19()
--------mnist_cnn.py(2KB)
--------mnist_plot.py(525B)
--------mnist_cnn_large.py(2KB)
--------mnist_mlp_baseline.py(1KB)
----chapter_27()
--------lstm_var_length.py(2KB)
--------lstm_char_seq_features.py(2KB)
--------lstm_char_seq_timesteps.py(2KB)
--------lstm_one_char_stateful.py(2KB)
--------lstm_char_seq_batch.py(2KB)
--------lstm_one_char.py(2KB)
----chapter_17()
--------decay_time_based.py(1KB)
--------decay_drop_based.py(1KB)
--------ionosphere.csv(75KB)
----chapter_24()
--------mlp_window.py(2KB)
--------international-airline-passengers.csv(2KB)
--------mlp_simple.py(2KB)
----chapter_16()
--------dropout_visible.py(2KB)
--------sonar.csv(86KB)
--------dropout_hidden.py(2KB)
--------baseline.py(2KB)
----chapter_21()
--------cifar10_plot.py(345B)
--------cifar10_cnn.py(2KB)
--------cifar10_cnn_large.py(2KB)
----chapter_13()
--------serialize_yaml.py(2KB)
--------serialize_json.py(2KB)
--------pima-indians-diabetes.csv(23KB)
----chapter_11()
--------sonar.csv(86KB)
--------sonar_baseline.py(1KB)
--------sonar_standardized.py(2KB)
--------sonar_standardized_smaller.py(2KB)
--------sonar_standardized_larger.py(2KB)
----chapter_07()
--------pima-indians-diabetes.csv(23KB)
--------first_mlp.py(793B)
----chapter_26()
--------lstm_dropout_gates.py(1KB)
--------lstm_dropout_layers.py(1KB)
--------lstm_cnn.py(1KB)
--------lstm_simple.py(1KB)
----chapter_28()
--------weights-improvement-19-1.9435.hdf5(1.06MB)
--------weights-improvement-47-1.2219-bigger.hdf5(3.07MB)
--------lstm_larger_gen_text.py(2KB)
--------lstm_small.py(2KB)
--------wonderland.txt(144KB)
--------lstm_gen_text.py(2KB)
--------lstm_larger.py(2KB)
----chapter_09()
--------sklearn_grid_search_params.py(2KB)
--------pima-indians-diabetes.csv(23KB)
--------sklearn_cross_validation.py(1KB)
----chapter_03()
--------tensorflow_example.py(384B)
----chapter_14()
--------checkpoint_best_model.py(1KB)
--------checkpoint_load.py(1KB)
--------pima-indians-diabetes.csv(23KB)
--------checkpoint_model_improvements.py(1KB)
----chapter_08()
--------manual_split.py(925B)
--------manual_cross_validation.py(1KB)
--------pima-indians-diabetes.csv(23KB)
--------automatic_split.py(704B)
----chapter_02()
--------theano_example.py(396B)
----chapter_15()
--------plot_history.py(1KB)
--------pima-indians-diabetes.csv(23KB)
----chapter_12()
--------boston_standardized.py(1KB)
--------housing.csv(48KB)
--------boston_baseline.py(1KB)
--------boston_standardized_larger.py(1KB)
--------boston_standardized_wider.py(1KB)
deep_learning_with_python.pdf