Every time a neural network learns to recognize a face, translate a sentence, or predict stock prices, gradient computation is working behind the scenes. This fundamental mechanism is what transforms a randomly initialized network into a powerful prediction machine. Understanding gradient computation isn’t just an academic exercise—it’s the key to comprehending how deep learning actually … Read more
The fundamental challenge in training machine learning models on noisy datasets lies in distinguishing genuine patterns from random fluctuations—a task that becomes critically dependent on how gradient descent processes the training data. Batch gradient descent computes gradients using the entire dataset before each parameter update, providing a deterministic, stable signal that averages out noise across … Read more
When training neural networks, practitioners face a fundamental question that significantly impacts both model quality and training efficiency: what batch size should I use? The answer isn’t simply “as large as your GPU memory allows” or “stick with the default.” The relationship between batch size and gradient noise scale reveals deep insights into the optimization … Read more
Training a machine learning model can be a lot like navigating a maze—you need to find the right path, and that path isn’t always obvious. One of the biggest challenges in this journey is figuring out the right learning rate. Too high, and your model may overshoot; too low, and it could take forever to … Read more
