Backpropagation is not magic and it is not new. It is the chain rule from first-year calculus, applied mechanically to a computational graph in reverse. This is what actually happens when a network learns: the two passes, a worked example in NumPy, why it runs backward instead of forward, where the gradients vanish and explode, and what the optimizer does with the numbers.
Deep-Learning
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Backpropagation: How Neural Networks Actually Learn -
Mixture of Experts Internals: Routing, Expert Parallelism, and Load Balancing MoE is now the default architecture for top-tier open models. This is how it actually works: router design, top-k token dispatch, auxiliary loss for load balancing, expert parallelism across GPUs, and what it means for inference serving — with DeepSeek-V3 and Mixtral as concrete case studies.