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Learning tensorflow eBook (PDF)
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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