Deep Neural Network Backpropagation
Follow data and gradients through an L-layer neural network with dimension rules and reusable NumPy formulas.
NumPy (Numerical Python) is a fundamental Python library for numerical computing. It provides powerful support for working with large, multi-dimensional arrays and matrices, along with a wide range of mathematical functions to operate on these arrays efficiently. NumPy forms the core of the scientific computing ecosystem in Python and is optimized for performance using C under the hood. It supports broadcasting, vectorized operations, linear algebra, random number generation, Fourier transforms, and integration with other libraries such as pandas, scikit-learn, TensorFlow, and PyTorch. NumPy arrays (ndarray) are more memory-efficient and faster than native Python lists, making them essential for data science, machine learning, and scientific research.
Follow data and gradients through an L-layer neural network with dimension rules and reusable NumPy formulas.
Implement a one-hidden-layer neural network from scratch and understand every forward and backward equation.
A beginner-friendly explanation of neural-network structure, notation, hidden layers, and forward propagation with a verified NumPy example.
A practical guide to logistic regression, from probability and cross-entropy to vectorized gradients and a complete NumPy implementation.
Replace slow loops with clear matrix operations and learn how to prevent silent broadcasting and shape errors in NumPy.
Every code block in this series is NumPy or PyTorch. This is the array vocabulary they both assume.