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 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: - jupyter notebook I built in this video: - my website: - my twitter: Useful links for practice: - Python Numpy tutorial from CS231n . We use instead of 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 - Another PyTorch intro to Tensor Exercises: E01: train a trigram language model, i.e. take two characters as an input to predict the 3rd one. Feel free to use either counting or a neural net. Evaluate the loss; Did it improve over a bigram model? E02: split up the dataset randomly into 80% train set, 10% dev set, 10% test set. Train the bigram and trigram models only on the training set. Evaluate them on dev and test splits. What can you see? E03: use the dev set to tune the strength of smoothing (or regularization) for the trigram model - i.e. try many possibilities and see which one works best based on the dev set loss. What patterns can you see in the train and dev set loss as you tune this strength? Take the best setting of the smoothing and evaluate on the test set once and at the end. How good of a loss do you achieve? E04: we saw that our 1-hot vectors merely select a row of W, so producing these vectors explicitly feels wasteful. Can you delete our use of in favor of simply indexing into rows of W? E05: look up and use instead. You should achieve the same result. Can you think of why we’d prefer to use instead? E06: meta-exercise! Think of a fun/interesting exercise and complete it. Chapters: 00:00:00 intro 00:03:03 reading and exploring the dataset 00:06:24 exploring the bigrams in the dataset 00:09:24 counting bigrams in a python dictionary 00:12:45 counting bigrams in a 2D torch tensor (“training the model“) 00:18:19 visualizing the bigram tensor 00:20:54 deleting spurious (S) and (E) tokens in favor of a single . token 00:24:02 sampling from the model 00:36:17 efficiency! vectorized normalization of the rows, tensor broadcasting 00:50:14 loss function (the negative log likelihood of the data under our model) 01:00:50 model smoothing with fake counts 01:02:57 PART 2: the neural network approach: intro 01:05:26 creating the bigram dataset for the neural net 01:10:01 feeding integers into neural nets? one-hot encodings 01:13:53 the “neural net“: one linear layer of neurons implemented with matrix multiplication 01:18:46 transforming neural net outputs into probabilities: the softmax 01:26:17 summary, preview to next steps, reference to micrograd 01:35:49 vectorized loss 01:38:36 backward and update, in PyTorch 01:42:55 putting everything together 01:47:49 note 1: one-hot encoding really just selects a row of the next Linear layer’s weight matrix 01:50:18 note 2: model smoothing as regularization loss 01:54:31 sampling from the neural net 01:56:16 conclusion