文件名称：TensorFlow Machine Learning Cookbook 源代码
文件大小：201KB
文件格式：ZIP
更新时间：2020-03-07 03:39:01
TensorFlow Machine Learning
TensorFlow Machine Learning Cookbook February 2017 Book Description TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production. What You Will Learn Become familiar with the basics of the TensorFlow machine learning library Get to know Linear Regression techniques with TensorFlow Learn SVMs with hands-on recipes Implement neural networks and improve predictions Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Take TensorFlow into production
【文件预览】：
TensreFlowMachineLearningCookbook_Code
----Chapter 2()
--------evaluating_models.py(6KB)
--------combining_everything_together.py(3KB)
--------layering_nested_operations.py(1000B)
--------loss_functions.py(4KB)
--------back_propagation.py(4KB)
--------multiple_layers.py(2KB)
--------operations_on_a_graph.py(629B)
--------batch_stochastic_training.py(3KB)
----Chapter 8()
--------deepdream.py(7KB)
--------introductory_cnn.py(7KB)
--------download_cifar10.py(3KB)
--------cnn_cifar10.py(12KB)
--------stylenet.py(7KB)
----Chapter 7()
--------text_helpers.py(7KB)
--------doc2vec.py(11KB)
--------word2vec_cbow.py(6KB)
--------bag_of_words.py(6KB)
--------tf_idf.py(6KB)
--------using_word2vec.py(6KB)
--------word2vec_skipgram.py(10KB)
----Software list.pdf(81KB)
----Chapter 6()
--------single_hidden_layer_network.py(3KB)
--------tic_tac_toe()
--------using_a_multiple_layer_network.py(6KB)
--------improving_linear_regression.py(5KB)
--------gates.py(2KB)
--------implementing_different_layers.py(9KB)
--------activation_functions.py(3KB)
----Chapter 1()
--------placeholders.py(528B)
--------data_gathering.py(4KB)
--------operations.py(1KB)
--------matrices.py(1KB)
--------tensors.py(2KB)
--------activation_functions.py(2KB)
----Chapter 9()
--------implementing_lstm.py(11KB)
--------seq2seq_translation.py(9KB)
--------implementing_rnn.py(6KB)
--------stacking_multiple_lstm.py(11KB)
----Chapter 5()
--------image_recognition.py(3KB)
--------nearest_neighbor.py(4KB)
--------address_matching.py(4KB)
--------mixed_distance_functions_knn.py(5KB)
--------text_distances.py(3KB)
----Chapter 3()
--------logistic_regression.py(4KB)
--------lin_reg_l1_vs_l2.py(3KB)
--------lin_reg_inverse.py(1KB)
--------lin_reg_tensorflow_way.py(2KB)
--------elasticnet_regression.py(2KB)
--------lasso_and_ridge_regression.py(3KB)
--------lin_reg_decomposition.py(2KB)
--------deming_regression.py(3KB)
----Chapter 11()
--------solving_ode_system.py(2KB)
--------k_means.py(5KB)
--------using_tensorboard.py(4KB)
--------genetic_algorithm.py(4KB)
----Chapter 10()
--------production_ex_train.py(9KB)
--------parallelizing_tensorflow.py(1KB)
--------production_tips_for_tf.py(3KB)
--------using_multiple_devices.py(2KB)
--------implementing_unit_tests.py(7KB)
--------production_ex_eval.py(4KB)
----Chapter 4()
--------nonlinear_svm.py(5KB)
--------multiclass_svm.py(5KB)
--------support_vector_regression.py(4KB)
--------linear_svm.py(4KB)
--------svm_kernels.py(5KB)
----README.md.txt(99B)