In this segment, you will learn about a library called **TensorFlow**, the knowledge of which will help you build your own neural network with much ease.

Please note that we are using Tensorflow primarily to understand what is happening behind the scenes. You are expected to be comfortable with is the high-level API Keras. In the next session, you will learn about Keras.

The objectives for the upcoming segments are given below:

- To learn about the capabilities of TensorFlow as an ML library, which specialises in deep learning
- To get an understanding of the coding paradigm of TensorFlow to perform simple coding tasks
- To understand the fundamentals of TensorFlow, such as its data structure, tensors, and certain mathematical operations that can be performed using this ML library

TensorFlow is an **open-source platform** for developing end-to-end machine learning (ML) solutions. The TensorFlow platform contains services such as TensorFlow.js, TensorFlow lite, and TensorFlow Extended, which are used to develop applications for browsers, mobile platforms, and large production environments, respectively.

The platform has all the tools needed to build a solution and deploy it on different platforms. One part of the complete TensorFlow environment is the **TensorFlow machine learning library**, which will be covered in this session.

TensorFlow is a **deep learning library** developed by **Google**. It is used widely in the industry for several different applications. Some of these applications include smart text in Gmail, Google Translate and Google Lens. Now, in the next video, Avishek will discuss TensorFlow and its features.

The features of TensorFlow make it a useful library for ML. In the upcoming segments, you will explore all these features in detail.

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