{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/1cb518695f7b40129bc5f0bf52d67dd7\" frameborder=\"0\" width=\"1658\" height=\"1243\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1243,"width":1658,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1243,"thumbnail_width":1658,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/1cb518695f7b40129bc5f0bf52d67dd7-f14d14fba17969d6.gif","duration":123.627,"title":"Deep Learning Breakdowns in Language Models","description":"This Loom walkthrough explains key deep learning breakthroughs for language processing, starting with how computers handle text through tokenization. It covers character, word, and subword tokenization and notes that BPE-style sub-level methods are preferred, followed by embeddings to represent words as vectors and visualize semantic relationships like king minus man plus woman equals queen using cosine similarity. It then moves through sequential models including RNN, LSTM, and GRU, and introduces attention mechanisms before covering modern transformer-based language models such as BERT and GPT. The discussion concludes with RLHF, including how human feedback and alignment support modern conversational AI systems."}