文件名称：Deep learning with Python in Tensorflow (book + complete code for 24 projects)
文件大小：6.92MB
文件格式：ZIP
更新时间：2021-03-04 08:27:01
Deep learning, CNN, RNN, Python
Welcome to Deep Learning With Python. This book is your guide to deep learning in Python. You will discover the Keras Python library for deep learning and how to use it to develop and evaluate deep learning models. In this book you will discover the techniques, recipes and skills in deep learning that you can then bring to your own machine learning projects. Deep learning does have a lot of fascinating math under the covers, but you do not need to know it to be able to pick it up as a tool and wield it on important projects and deliver real value. From the applied perspective, deep learning is quite a shallow field and a motivated developer can quickly pick it up and start making very real and impactful contributions.
【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