GPUs & CUDA Basics

Summary

GPUs parallelize the matrix operations behind neural networks, cutting training time from days to hours — understanding CUDA basics helps you debug device and memory issues.

How to Learn This

1Train a model on GPU vs. CPU and compare training time directly.
2Learn to diagnose and fix common CUDA out-of-memory errors.
3Understand mixed-precision training as a way to speed up GPU training further.

Dropout & Regularization in DL

Digit Classifier (MNIST)

More topics in Deep Learning Foundations

Neural Network Basics Perceptrons Activation Functions Forward & Backward Propagation Loss Functions Gradient Descent Variants (SGD, Adam)PyTorch Basics TensorFlow/Keras Basics Batch Normalization Dropout & Regularization in DL

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