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Hi everyone. Today we are continuing our implementation of Make More. Now in the last lecture, we implemented the bigram language model. And we implemented it both using counts and also using a super simple neural network that has a single linear layer. Now this is the, Jupyter Notebook that we built out last lecture. ... |
大家好。今天，我们将继续实施“Make More”计划。在上一讲中，我们实现了二元语言模型。我们既使用计数来实现它，也使用具有单个线性层的超级简单的神经网络来实现它。这是我们在上节课中制作的 Jupyter Notebook。我们发现，我们解决这个问题的方法是，我们只查看单个前一个字符，并预测序列中下一个字符的分布。我们通过计数并将其标准化为概率来实现这一点，这样这里的每一行总和都为 1。现在，如果您只有一个先前上下文的字符，那么这一切都很好。这很有效，而且很容易实现。当然，这个模型的问题在于，由于您只采用一个上下文字符，因此该模型的预测效果不是很好。因此，该模型并没有产生听起来很卑鄙的东西。但是，这种方法的问题是，如果我们在预测序... |
"Everyone. Today we are continuing our implementation of Make More. Now in the last lecture we imple(...TRUNCATED) |
"每个人。今天，我们将继续实施“Make More”计划。在上一讲中，我们按照 Be(...TRUNCATED) |
"Hi everyone. So today we are once again continuing our implementation of Make More. Now so far we'v(...TRUNCATED) |
"大家好。所以今天我们再次继续实施“Make More”计划。到目前为止，我们已(...TRUNCATED) |
"Hi, everyone. Today, we are continuing our implementation of Make More, our favorite character leve(...TRUNCATED) |
"大家好。今天，我们继续实现我们最喜欢的字符级语言模型 Make More。现在(...TRUNCATED) |
"Hi, everyone. So by now, you have probably heard of ChatGPT. It has taken the world and the AI comm(...TRUNCATED) |
"大家好。所以现在，你可能已经听说过 ChatGPT。它席卷了世界和人工智能社(...TRUNCATED) |
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