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Creation, Initializing, Saving and Restoring TensorFlow variables
Feeding, Reading and Preloading TensorFlow data
How to use TensorFlow infrastructure to train models at scale
Visualizing and Evaluating models with TensorBoard
Inputs and Placeholders
Build the Graph
Train the model
Evaluate the model.
Build the eval graph
The perceptron learning algorithm
Binary classification with the perceptron
Document classification with the perceptron
Limitations of the perceptron
Support Vector Machines
Kernels and the kernel trick.
Maximum margin classification and support vectors
Artificial Neural Networks
Nonlinear decision boundaries
Feedforward and feedback artificial neural networks
Minimizing the cost function
Improving the way neural networks learn
Convolutional Neural Networks
Launching and training the model.
Evaluating a model.
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