Summary of The spelled-out intro to language modeling: building makemore. Part 2: MLP

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00:00:00 - 01:00:00

The video explains how to build a language model by training a neural network on a training set of data. The development set is used to set hyperparameters, and the test set is used to evaluate the performance of the model. The speaker also explains how to split the data into train, dev, and test sets.

00:00:00 This 1-paragraph summary explains how a neural network is used to model the next character in a sequence, following a paper by Benguetal. The network is initialized with words at random, and then tunes its weights using backpropagation. This allows for models that are able to predict words that have never been seen before, even if the exact phrase has not been seen in training data.
00:05:00 This video explains how a neural network can be used to learn new words by predicting similar phrases from previously seen words. The network is initialized with a vocabulary of 17,000 words and a lookup table called c. The network is trained on a training set of new words and the predicted probability of each word is outputted.
00:10:00 In this video, the creator demonstrates how to build a language model to predict the labels for a set of words. The neural network is initialized with a lookup table that maps each input character to a random number in a two-dimensional space. Each input character is then embedded in this space using a matrix. Finally, the predictions are made using the neural network.
00:15:00 The problem is that the matrix multiplication in the language modeling algorithm will not work if the input tensor is in the shape of 32 x 3 x 2, which is the shape of the weights and biases in the hidden layer. The solution is to concatenate the inputs together so that the matrix multiplication can take place.
00:20:00 The video discusses how to use the Torch library to perform various language modeling operations, including concatenating tensors of different sizes. The code is designed to be generalizable to future block sizes, but a more efficient way is shown if the block size is changed at runtime. This efficient way uses a view to represent the tensor as an n-dimensional tensor, which avoids copying, moving, or creating memory.
00:25:00 The video demonstrates how to build a language model using the "logistic" neural net shape, which has 32 by 27 neurons. The logits, or output of the neural net, are multiplied by a "w2" bias and a "b2" logistic bias, and then exponentiated to get fake counts.
00:30:00 This video explains how to create a language model that can predict the next character in a sequence. First, the language model is normalized into a probability. Then, the probabilities are calculated for each character in the sequence, and the loss is minimized using a functional cross entropy loss function. Finally, the logits and targets are passed in, and the loss is calculated.
00:35:00 The spell-out intro to language modeling starts by explaining how the forward and backward passes work, and then goes on to show how a neural network can be trained using these passes. The forward pass is easy to overfit, but the backward pass is effective at reducing overfitting.
00:40:00 In this video, the author introduces language modeling, which is the process of predicting the likely outcomes of a given input, given a set of training data. The author demonstrates how to perform forward and backward passes on a mini batch of the data, and how to use storage.randint to generate a set of random numbers between 0 and 5. This allows the author to quickly perform many iterations of the optimization process, resulting in better quality gradients and a faster optimization process.
00:45:00 The speaker is describing how they determined a learning rate for a language model, and how it works as follows. They reset the parameters to the initial settings and printed in every step, but only did 10 steps. They then used a learning rate indexing function to find a learning rate between negative zero points between 0.001 and 1, and stepped linearly between these values. The learning rates were then used to keep track of the losses that resulted from the optimization.
00:50:00 In this video, a trainer shows how to set a learning rate for a neural network. The trainer describes how to find a good learning rate and how to adjust it if it starts to plateau. Finally, the trainer demonstrates how to decay the learning rate and achieve a trained network.
00:55:00 In this video, the transcript of which is followed by a one-paragraph summary, the speaker explains how to build a language model. The 80% of the data used to train the model is used to optimize the parameters, and the 10% of the data used for development is used to set hyper parameters. The speaker also explains how to split the training data into train, dev, and test sets, and how to evaluate the model's performance on the test set.

01:00:00 - 01:15:00

This video explains how to build a language model using Google Colab. The presenter discusses how to optimize the model for better performance, including increasing the number of input characters and playing with the neural network size.