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The spelled-out intro to language modeling: building makemore by Andrej Karpathy
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https://www.youtube.com/watch?v=PaCmpygFfXo We implement a bigram character-level language model, which we will further complexify in followup videos into a modern Transformer language model, like GPT. In this video, the focus is on (1) introducing torch.Tensor and its subtleties and use in efficiently evaluating neural networks and (2) the overall framework of language modeling that includes model training, sampling, and the evaluation of a loss (e.g. the negative log likelihood for classification). Links: - makemore on github: https://github.com/karpathy/makemore - jupyter notebook I built in this video: https://github.com/karpathy/nn-zero-t... - my website: https://karpathy.ai - my twitter: https://twitter.com/karpathy Useful links for practice: - Python + Numpy tutorial from CS231n https://cs231n.github.io/python-numpy... . We use torch.tensor instead of numpy.array in this video. Their design (e.g. broadcasting, data types, etc.) is so similar that practicing one is basically practicing the other, just be careful with some of the APIs - how various functions are named, what arguments they take, etc. - these details can vary. - PyTorch tutorial on Tensor https://pytorch.org/tutorials/beginne... - Another PyTorch intro to Tensor https://pytorch.org/tutorials/beginne...
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