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