GenZ to AI Enz
A series on AI engineering for CS students and early-career engineers — from the basics of deep learning to building production LLM systems.
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GenZ to AI Enz: A Roadmap for CS Grads Breaking into AI
A complete series taking CS students and early-career engineers from zero ML knowledge to building real AI systems with LLMs and agents.
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GenZ to AI Enz: Series Index
Full table of contents for the GenZ to AI Enz series - every post and walkthrough in order.
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What is a Neural Network?
A neural network explained from scratch - neurons, weights, layers, and the forward pass - no ML background required.
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Activation Functions: Why ReLU, GELU, and SiLU Exist
Why stacking linear layers isn't enough, and how activation functions like ReLU, GELU, and SiLU give neural networks their power.
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Loss Functions: How a Neural Network Knows It's Wrong
What loss functions are, how MSE and cross-entropy work, and why picking the wrong one breaks your model even if everything else is right.
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Gradient Descent and Backpropagation: How a Network Actually Learns
How gradient descent uses the loss to update weights, and how backpropagation computes the gradients that make it possible.
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Optimizers: SGD, Momentum, Adam, and AdamW
Why plain gradient descent isn't enough, and how SGD, momentum, Adam, and AdamW each fix a problem the previous one had.
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Normalization: BatchNorm, LayerNorm, and Why Transformers Need a Different One
Why activations drift as they pass through deep networks, and how BatchNorm and LayerNorm fix it in different ways.
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Dropout and Overfitting: Teaching a Network Not to Cheat
What overfitting is, why it happens, and how dropout stops a network from memorising the training data.
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Bias, Variance, and the Tradeoff Every Model Faces
Why models fail in two opposite ways — being too rigid or too sensitive — and how to find the sweet spot between them.