Browsing Tag
neural network
57 posts
What Is the Purpose of a Confusion Matrix in Classification Tasks
When I first started building classification models, I judged everything by a single number: accuracy. It felt intuitive…
What Is the Role of Mini-Batch Training in Neural Networks
When I trained my very first neural network, I made the mistake of feeding the entire dataset through…
Understanding Momentum in Optimization Algorithms: A Complete Guide
When I first started training neural networks, I remember watching the loss curve bounce around like a pinball…
Stochastic Gradient Descent (SGD): The Optimizer That Started It All
Every deep learning optimizer in use today — Adam, RMSProp, AdaGrad, and dozens of lesser-known variants — is…
The Adam Optimizer: How It Works and Why It’s the Default Choice
If you’ve trained even a handful of neural networks, you’ve almost certainly typed optimizer = Adam(...) without thinking…
The Role of Learning Rate Scheduling in Training a Neural Network
If there’s one hyperparameter that can single-handedly make or break a training run, it’s the learning rate. Set…
Early Stopping in Neural Network Training: A Practical Guide
There’s a particular moment in training a neural network that I’ve come to watch for closely: the point…
Dropout Regularization: What It Is and How It Works
Of all the regularization tricks in deep learning, dropout is probably the one that sounds the most counterintuitive…
L1 vs. L2 Regularization: What’s the Difference?
Regularization is one of those topics that seems simple on the surface — “just add a penalty term…
The Role of L1 and L2 Regularization in Neural Networks
Neural networks, especially large ones, are almost absurdly good at memorizing whatever you feed them. Give a big…