Gradient Descent, Machine Learning, Neural Networks, Optimization, Linear Regression.
combines the advantages of both previous methods. By using mini-batches, the algorithm achieves a balance between stability and computational efficiency. It reduces the noise introduced by individual samples and provides a more accurate estimate of the true gradient. V. The Importance of Learning