In this post, we are going to get a basic understanding of:

- What is
**TensorFlow**? - What is offered in the context of machine learning?
- Tensorflow used in applications
- Types of
**TensorFlow**

**TensorFlow**:

**TensorFlow**

It is a free and open-source platform for high-performance numerical computation, specifically for **machine learning (ML)** and **Deep learning (DL)**. It has a flexible architecture and can be deployed across a variety of platforms like **CPUs,** **GPUs**, and **Google TPUs** as well as mobile and edge devices.

**Tensorflow in Machine Learning:**

**Tensorflow in Machine Learning:**

Tensorflow makes it easy to build and deploy Machine Learning solutions. So, this means that it’s not a simple framework just to build a machine learning project but a complete ecosystem that provides tools at each stage of the entire machine learning workflow:

- Data preprocessing to feature engineering
- Model training to model serving
- Data Pipelines to model inference

**TensorFlow Used in Application:**

**TensorFlow Used in Application:**

- Search Engines
- Text Translation
- Image Captioning
- Interpreting
- Recommendation Systems
- Weather forecasting

A tensor is simply a typed multi-dimensional array. It can be **0** to **N-dimensional**.

**Types of Tensorflow: **

**Types of Tensorflow:**

There we have different types of Tensors

__ Zero-dimensional:__ Which we call the Scalar. As you can see on the screen it has only the magnitude.

** One-dimensional:** which we usually call the Vector, it has the magnitude and the direction as you can see on the screen.

** Two-dimensional:** which we usually call a Matrix, this can be represented as a table of numbers.

__ Three-dimensional:__ which is also a matrix but it can be represented as a cube.

__ N-dimensional: __represented as a matrix.

Great, at this stage you should be familiar with TensorFlow, what it is, why we should use it along the most basic concept of Tensors.

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