A model widely used in traditional statistics is the linear regression model. In this article, the objective is to follow the step-by-step implementation of this type of models. We are going to represent a simple linear regression structure.

For our study, we will analyze the age of the children on the **x** axis and the height of the children on the **y** axis. We will try to predict the height of the children, using their age, applying simple linear regression.[in TF finding the best W and b]

Parameter | Description |
---|---|

train_X | np array with x dimension of information |

train_Y | np array with y dimension of information |

I used TensorBoard sintaxis to track the behavior of some parts of the model, cost, train and activation elements.

```
with tf.name_scope("") as scope:
```

Imports used:

```
import numpy as np
import tensorflow as tf
```

Type of application and language used:

I have used a traditional console implementation app type, developed in Python, to represent the example.

Version of TensorFlow used:

1.0.1

Conceptual **academic** example/reference extracted from here:

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