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
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 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:
- Search Engines
- Text Translation
- Image Captioning
- Recommendation Systems
- Weather forecasting
A tensor is simply a typed multi-dimensional array. It can be 0 to N-dimensional.
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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