Relationship of Tensors and Neural Networks Computing (Basic Concept)

1. The Story of “Brainy the AI Learner”

Characters:

  • Brainy: A young AI trying to learn how to recognize animals.
  • Tess: A magical cube-shaped messenger who stores and delivers information (she’s a Tensor!).
  • Neura: A group of wise but quirky friends (Neurons) who process messages and help Brainy learn.
  • Teacher AI: The mentor who adjusts everything to help Brainy learn better each day.

A. Tess, the Messenger (Understanding Tensors)

One day, Brainy wanted to learn how to identify whether a picture shows a cat or a dog.

Tess stepped in and said,

“I can store the picture for you in my cube-shaped body! I’m made of rows, columns, and layers—just like a cube made of little boxes. Each box holds a number from the picture, like color brightness or pixel intensity!”

Think of Tess (Tensor) like a spreadsheet or a Rubik’s cube of numbers, where:

  • A 1D tensor is a list of numbers.
  • A 2D tensor is like a table (rows and columns).
  • A 3D tensor can hold a stack of images.
  • Higher dimensions? No problem—Tess is magical that way!

Tess delivered this cube of numbers to Neura for processing.

B. Neura, the Processor (Neural Network Begins)
Neura, a network of friendly neurons, looked at the numbers from Tess.

Each Neuron:

  • Received numbers from Tess (inputs).
  • Multiplied them with some “importance values” (called weights).
  • Added a “bias” to allow for flexibility.
  • Passed the result through a gate (called an activation function) to decide: “Should I say Yes, No, or stay quiet?”

These outputs were then sent to the next group of neurons… and then to the next…
Like whispering messages in a circle of friends, until it reached the final neuron who said:

“Hmm… I think this is a dog!”

C. Teacher AI, the Coach (Learning Happens)
But Brainy wasn’t always right.

Sometimes, Teacher AI said: “Oops, that was actually a cat.”

So Teacher AI went back through Neura’s network and said:

  • “Hey, neuron #5, you gave too much importance to the ear shape—reduce your weight a bit.”
  • “Neuron #2, you ignored the tail! Let’s increase your attention to that.”

This adjusting process is called backpropagation—like giving feedback to each neuron.
With many rounds of this training, Brainy got better and better at telling dogs from cats.

D. Why This Matters in AI Computing

Here’s the real-world twist:

  • Images → are stored as tensors (3D: height × width × color channels).
  • Text → becomes tensors after converting words to numbers (via embeddings).
  • Sound → is sliced into wave patterns and stored as tensors too.

Then, Neural Networks process those tensors:

  • CNNs for images.
  • RNNs for sequences/text.
  • Transformers for complex language understanding.

Together, Tess (tensor) and Neura (neural networks) form the core building blocks of how AI sees, hears, reads, and thinks.

Moral of the Story

Whenever we hear about AI doing amazing things, just remember:

Tensors are how AI stores information. Neural networks are how AI learns from it.

And every time AI gets better, it’s because Teacher AI keeps tweaking the network, helping Brainy understand the world a little better.

Relationship of Tensors and Neural Networks Computing (Basic Concept) – Tensors & Neural Networks example with Simple Python