GPUs & Training Basics

Summary

Understanding why deep learning relies on GPUs (massively parallel matrix operations), and the basics of batch size, epochs, and training time trade-offs when working with limited compute.

How to Learn This

1Train the same small model on CPU versus GPU (e.g. via Colab) and compare training time.
2Learn how batch size affects both training speed and memory usage.
3Understand why free/cheap GPU access (Colab, Kaggle) is usually enough for learning projects.

Regularization in Deep Learning (Dropout, Batch Norm)

Train an Image Classifier

More topics in Deep Learning Fundamentals

Neural Network Basics Activation Functions Backpropagation & Gradient Descent Building Networks With TensorFlow / Keras Building Networks With PyTorch Convolutional Neural Networks (CNNs)Recurrent Neural Networks (RNNs) & LSTMs Transfer Learning Regularization in Deep Learning (Dropout, Batch Norm)

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