🍃Day 23 was all about learning the evolution of language models and coding RNNs from scratch in Python. From N gram language models —> Word2vec —> Recurrent neural network (RNN)—> Long short term memory networks(LSTM) —> Transformers, we have come a long way. 🍃But what is language modeling? 🤔 Language modeling serves as the backbone of natural language processing, predicting the next word in a sequence to enable meaningful communication. From autocomplete suggestions to virtual assistants, its applications are deeply woven into our daily interactions. This evolution traces back to the pre-deep learning era, where n-gram models laid the foundation for understanding and predicting sequences of words. 🍃N-gram language models, ranging from bigrams to trigrams, relied on a probability-based approach. These models predicted the likelihood of the next word based on historical n-grams, a fixed window of preceding words. 🍃The transition from probability-based models to neural networks ...