Tensor
A tensor is a fundamental data structure in machine learning, used to represent multi-dimensional arrays. Think of it as a generalization:
- 0D → scalar
- 1D → vector
- 2D → matrix
- nD → tensor
Relevance in AI
Tensors are the backbone of frameworks like TensorFlow (which literally takes its name from the concept) and PyTorch. They provide a consistent way to encode and manipulate data, whether it’s an image batch, a sequence of text embeddings, or time-series signals.
Examples
- A batch of 32 images of size 224×224 with 3 channels (RGB) is a 4D tensor.
- Word embeddings in NLP are stored as 2D tensors (number of words × embedding size).
Why it matters
Because optimization in deep learning boils down to operations like matrix multiplication, having tensors as a unified structure allows hardware (GPUs, TPUs) to process billions of values in parallel. Without tensors, modern deep learning would not scale.
Tensors can be thought of as the fundamental data carriers of deep learning. While their definition is rooted in linear algebra, in practice they serve as containers that let AI systems handle everything from single numbers to massive multi-dimensional datasets. Their abstraction hides much of the underlying complexity, allowing developers to write concise code while hardware acceleration libraries (CUDA, cuDNN, MKL) optimize performance under the hood.
One of the key strengths of tensors is their ability to bridge mathematics and computation. Operations such as dot products, convolutions, or matrix factorizations can be uniformly expressed as tensor manipulations. This makes tensors not only convenient but also highly composable, enabling the design of complex neural architectures with relative ease.
Tensors also provide the foundation for automatic differentiation, which is essential for training neural networks. By tracking operations in computational graphs, frameworks like PyTorch and TensorFlow can compute gradients with respect to tensors automatically, powering the backpropagation process.
