The spelled-out intro to language modeling: building makemore

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-to-hero/blob/master/lectures/makemore/makemore_part1_bigrams.ipynb - my website: https://karpathy.ai - my twitter: https://twitter.com/karpathy - (new) Neural Networks: Zero to Hero series Discord channel: https://discord.gg/3zy8kqD9Cp , for people who'd like to chat more and go beyond youtube comments Useful links for practice: - Python + Numpy tutorial from CS231n https://cs231n.github.io/