TensorFlow 机器学习 Cookbook on Windows Pc

tensorflow 0.1.0 documentation
0: TensorFlow Machine Learning Cookbook (version: 0.1.0)
1: How does TensorFlow work
15: declaration of variables and tensors
30: Use placeholders and variables
38: Matrix
54: operator declaration
62: Load activation function
72: Data Resources
83: Resource Library
89: learning modules in this chapter
100: calculation graph
101: Hierarchical nesting operations
102: Multi-layer operation
103: Load loss function
104: Load backpropagation
105: Random and batch training
106: combined training
107: Model Evaluation
108: Learning modules in this chapter
111: Matrix transpose
112: Matrix Factorization
113: Linear regression of TensorFlow
114: Loss function for linear regression
115: Deming Return (Full Return)
116: Lasso regression and Ridge regression
117: The Return of Elastic Net
118: Logistic regression
119: Learning modules in this chapter
122: Introduction
123: Linear Support Vector Machine
124: regression linear regression
125: Core in TensorFlow
126: Nonlinear Support Vector Machine
127: Multi-class support vector machine
128: Learning modules in this chapter
131: Introduction
132: Use of Nearest Neighbor Method
133: Text distance function
134: Calculate the mixed distance function
135: Address match
136: Near neighbor method for image processing
137: Learning modules in this chapter
140: Introduction
141: Load operation door
142: Gate operation and activation function
143: Load a layer of neural network
144: Load a multilayer neural network
145: Use a multilayer neural network
146: Linear model prediction improvement
147: Neural Network Learning Tic-Tac-Toe
148: Learning modules in this chapter
151: Introduction
152: Bag of Words
153: Term Frequency-Inverse Text Frequency (TF-IDF)
154: Use Skip-Gram
155: CBOW (Continuous Bag fo Words)
156: Word2Vec application example
157: Doc2Vec Sentiment Analysis
158: Neural Network Learning Tic-Tac-Toe
159: Learning modules in this chapter
162: Introduction
163: Simple CNNs
164: Advanced CNNs
165: Retrain an existing architecture
166: Use Stylenet/Neural-Style
167: Using Deep Dream
168: Introduction
169: Convolutional neural network model for spam detection
170: LSTM model for text generation
171: Stacked multi-layer LSTM
172: Create segment-to-segment model translation (Seq2Seq)
173: Training Siamese similarity measurement
174: unit test
175: Use multiple actuators (devices)
176: TensorFlow parallelization
177: TensorFlow development tips
178: TensorFlow development example
179: Visualization of computational graphs (using Tensorboard)
180: genetic algorithm
181: K-means cluster analysis
182: Solve system ordinary differential equations
183: Random Forest
184: Keras in TensorFlow

TensorFlow 机器学习 Cookbook

Allows an application to read from external storage.
Allows an application to write to external storage.
Allows applications to open network sockets.
Allows applications to access information about networks.
Allows using PowerManager WakeLocks to keep processor from sleeping or screen from dimming.