Machine Learning Guide

Explains the fundamental differences between tensor dimensions, size, and shape, clarifying frequent misconceptions—such as the distinction between the number of features (“columns”) and true data dimensions—while also demystifying reshaping operations like expand_dims, squeeze, and transpose in NumPy. Through practical examples from images and natural language processing, listeners learn how to manipulate tensors to match model requirements, including scenarios like adding dummy dimensions for grayscale images or reordering axes for sequence data. Links Notes and resources at ocdevel.com/mlg/mla-5 Try a walking desk stay healthy & sharp while you learn & code Definitions Tensor: A general term for an array of any number of dimensions. 0D Tensor (Scalar): A single number (e.g., 5). 1D Tensor (Vector): A simple list of numbers. 2D Tensor (Matrix): A grid of numbers (rows and columns). 3D+ Tensors: Higher-dimensional arrays, such as images or batches of images. NDArray (NumPy): Stands for "N-