This episode explores the evolution and inner workings of neural networks, specifically focusing on their application in language modeling. Against the backdrop of the historical challenges in training deep neural networks, the speaker delves into key advancements like improved regularization techniques (including dropout) and optimization algorithms (like Adam). More significantly, the discussion pivots to language models, explaining their function in predicting the probability of word sequences and contrasting older N-gram models with newer neural network approaches. For instance, the limitations of N-gram models due to sparsity and storage issues are highlighted, while the advantages of neural networks in overcoming these are emphasized. The speaker then introduces recurrent neural networks (RNNs) as a powerful architecture for language modeling, explaining their mechanism of processing sequential data and their application in text generation. Finally, the episode showcases examples of text generation using RNNs trained on different corpora, illustrating their capabilities and limitations, and hinting at the future of language models beyond RNNs.
