Understanding Tensors: A Practical Guide for Deep Learning Enthusiasts

Photo by Diana Polekhina on Unsplash

Tensors are at the heart of Deep Learning, yet many aspiring AI practitioners skip over truly understanding them. Instead, they jump straight into building models, only to struggle when things go wrong. If you’ve already started your deep learning journey and want to strengthen your foundation, this guide is for you.
Whether you are using PyTorch or Tensorflow, mastering tensors concept is essential for your journey.

In this article, we’ll demystify tensors — not with heavy mathematics, but with a clear, practical approach. Let’s dive in! 🚀

What Are Tensors and Why Are They Important? 🤔

At their core, tensors are a generalization of matrices, crucial for computing outputs and updating weights in neural networks. But let’s keep it simple: think of a tensor as just a structured way to store numbers in memory.

For example, imagine a list of 120 numbers, ranging from 1 to 120. We can visualize it as a row with 120 columns:

Or, we could reshape it into a column with 120 rows:

Either way, the key takeaway is that the total number of elements remains 120. Understanding this will help you grasp more complex tensor operations later on. Let’s dive…