Real-World Examples of 0D, 1D, 2D, 3D, 4D and 5D Tensors
Data representation in neural networks: Neural Networks and Deep Learning Course: Part 4
This is Part 4 of our Neural Networks and Deep Learning Course as introduced here. Read Part 1, Part 2 and Part 3 if you haven't.
Introduction
Tensors are the basic data structure in machine learning and deep learning models. A tensor can be considered as a container for numerical data (numbers).
In neural networks, data is represented by using tensors. The input layer of a neural network holds text, speech, audio, images, videos or any other kind of data as tensors that takes numbers. We perform tensor operations (tensor addition, multiplication, reshaping, etc.) throughout the network with these tensors of numerical data.
What is a tensor?
The term “tensor” is a technical term in the context of deep learning. The deep learning library “TensorFlow” was named keeping tensors in mind.
Tensors are nothing but multi-dimensional NumPy arrays that we’re already familiar with!
Note: If you want to get hands-on experience in creating tensors with NumPy and TensorFlow, read my “Let’s Create Tensors like NumPy…
