Python Deep Learning

Neural networks explained the way you wish someone had explained them to you. Twelve tutorials build from the perceptron through activation functions and backpropagation to real networks in PyTorch and TensorFlow, then CNNs, transfer learning, LSTMs, and word embeddings. Every concept comes with code you can run.

The Attention Mechanism From Scratch in PyTorch

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Every large language model you have used is, at its core, dot products, a softmax, and a weighted sum. That is the attention mechanism, and in this workshop you build it from scratch in PyTorch, tensor by tensor. You will compute Query, Key, and Value by hand on a four-word sentence, read a real heatmap, assemble multi-head attention, add causal masking, and train a tiny model that predicts text. “If you can compute it by hand once, you can debug ... Read More

Deep Learning Capstone: Image Classifier on Hugging Face

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A folder of your own photos on one end, a public web demo anyone can click on the other. This image classification project covers that whole distance: fine-tune a pretrained network in PyTorch, push past 90% validation accuracy, read the confusion matrix honestly, then deploy free to Hugging Face Spaces with a proper model card. Everything runs on a plain laptop CPU, no GPU and no paid account. “A model that only lives in your notebook does not exist to ... Read More

PyTorch Dataset and DataLoader: The Real Training Loop

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You trained your first network on a toy set that fit in one tensor, and it felt easy. Then real data arrives, a CSV with thousands of rows or an image folder too big for memory, and the PyTorch DataLoader is the tool built for exactly that. Here you pair it with a custom Dataset, feed a network from a CSV and an image folder, run the five-step loop from memory, validate honestly, and save a checkpoint you can resume ... Read More