Learning tensorflow eBook (PDF)
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Chapters
Chapters
- Chapter 1: Getting started with tensorflow
- Chapter 2: Creating a custom operation with tf.py_func (CPU only)
- Chapter 3: Creating RNN, LSTM and bidirectional RNN/LSTMs with TensorFlow
- Chapter 4: How to debug a memory leak in TensorFlow
- Chapter 5: How to use TensorFlow Graph Collections?
- Chapter 6: Math behind 2D convolution with advanced examples in TF
- Chapter 7: Matrix and Vector Arithmetic
- Chapter 8: Measure the execution time of individual operations
- Chapter 9: Minimalist example code for distributed Tensorflow.
- Chapter 10: Multidimensional softmax
- Chapter 11: Placeholders
- Chapter 12: Q-learning
- Chapter 13: Reading the data
- Chapter 14: Save and Restore a Model in TensorFlow
- Chapter 15: Save Tensorflow model in Python and load with Java
- Chapter 16: Simple linear regression structure in TensorFlow with Python
- Chapter 17: Tensor indexing
- Chapter 18: TensorFlow GPU setup
- Chapter 19: Using 1D convolution
- Chapter 20: Using Batch Normalization
- Chapter 21: Using if condition inside the TensorFlow graph with tf.cond
- Chapter 22: Using transposed convolution layers
- Chapter 23: Variables
- Chapter 24: Visualizing the output of a convolutional layer
