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If you are beginning to learn machine learning, the number of model names can make the field seem much more complicated than it actually is, but most models can be organized into a few major families based on how they learn and the kinds of problems they are designed to solve. Linear and logistic regression learn relatively simple relationships between variables, while decision trees, random forests, and gradient-boosting models make predictions through combinations of decision rules. K-nearest
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Andrej Karpathy's explaination of Bigram Language Model explained Bigram LMs, though simple, it provides powerful insight into the inner mechanics of how tokens are processed in language models. This is a pre-amble for what's next: GPT, which is the 2nd part of the series The Bigram model here incorporates: Tokenization, Vocabulary, Negative Loss Function, Cross Entropy, Logits, SoftMax, Optimizer, and AdamW These are essential ingredients to understand in order to build our knowledge on how LLM
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