**Deep Learning Framework :**

TensorFlow is a computational framework used to build Machine Learning models.

**Mobile Deployments :**

TensorFlow is good for mobile deployments as it provides easier understanding of data flow in the framework.

**Google's Brainchild**

Actively developed by all the awesome people at Google.

**High Performance**

TensorFlow serving is well known for being used for server-side deployments.

**Linear Regression**

- Statistics :- Modelling the relationship between a scalar dependent variable and one or more explanatory variables
- Technique :- Linear Regression is a statistical used to measure the relationship between the variables
- Let's have an Example :- A simple Linear Regression can be:

**y = w * x + b**

**Rock Or a mine prediction :**

You have been hired by US navy to create a model, that can detect the difference between a mine and a rock.

A noval mine is a self-contained explosive device placed in water to damage or destroy surface ships or submarines.

**How to create this model ?**

Start

**→**Read the Datasets

**→**Define features

**→**Encode the dependent variable

**→**Divide the datasets into two parts for training and testing

**→**TensorFlow data structure for holding features, labels etc.

**→**Implement the model

**→**Train the model

**→**Reduce MSE (actual output - desired output)

**→**Make prediction on the test data.

**→**End.

Tensors are the standard way of representing data in TensorFlow (deep learning).

Tensors are multidimensional arrays, an extension of two-dimensional tables (matrices) to data with higher dimension.

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