1. Multiple level APIs:
(1) lowest level: TensorFlow Core, if you need fine controls on the model
(2) higher levels: built on Core, easier to use, e.g. tf.contrib.learn

2. Tensor: an array of primitive values. Rank: how many dimensions. Shape: a vector, containing the numbers of elements in each dimension.
e.g. [[[1., 2., 3.]], [[7., 8., 9.]]] has rank 3, shape [2,1,3]

3. Import library

import tensorflow as tf

4. computational graph: a graph of nodes, each node has 0 or more tensors as inputs and 1 tensor as output

5. session: contains the control and state of TensorFlow runtime

sess = tf.Session();[node1, node2])

6. Combine Tensor nodes and operations (also nodes), e.g.

node3 = tf.add(node1, node2)

7. Use TensorBoard to draw computational graph

8. Placeholder: like a parameter or variable, used to define a function

9. Variable: constructed with a type and initial value, used to add trainable parameters

10. loss function: measure the distance from model to ground truth

11. optimizer: change each variable to minimize loss. e.g. gradient descent

optimizer = tf.train.GradientDescentOptimizer(0.01)

12. estimator: front end to invoke training and evaluation,e.g. to do linear regression

estimator = tf.contrib.learn.LinearRegressor(feature_columns=features)

13. Use tf.contrib.learn.Estimator to define custom model

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