Tensors in Deep Learning 🧠

Tensors are the core data structures in deep learning and machine learning, representing multi-dimensional arrays. They generalize scalars, vectors, and matrices to higher dimensions. Here's a breakdown:

📌 What are Tensors?

  • Scalar: 0-dimensional tensor (e.g., a single number: 5)
  • Vector: 1-dimensional tensor (e.g., [1, 2, 3])
  • Matrix: 2-dimensional tensor (e.g., [[1, 2], [3, 4]])
  • 3D Tensor: 3-dimensional array (e.g., a batch of images: [batch, height, width])
Scalar_Vector_Matrix_3D_Tensor

🧮 Key Properties

  • Rank: Number of dimensions (e.g., rank-0 for scalars, rank-1 for vectors)
  • Shape: Dimensions of the tensor (e.g., shape=(2,3) for a 2x3 matrix)
  • Data Type: Specifies the type of elements (e.g., float32, int64)

🛠️ Applications in Deep Learning

Tensors are used to represent:

  • Input data (e.g., images, text)
  • Model parameters (e.g., weights in neural networks)
  • Intermediate outputs during computation

Example: In a convolutional neural network (CNN), images are often represented as 3D tensors with shape [height, width, channels].

📚 Expand Your Knowledge

For a deeper dive into tensor operations and their role in machine learning, visit our Tensor Basics Guide.

Stay curious! 🚀