[D] From RNNs to GPT4 - 10 years of NLP research explained in 50 concepts

In this video from my YT channel, I explain 50 concepts that cover the basics of NLP like Tokenization and Word Embeddings, to seminal work like RNNs, Seq2Seq, Attention, to innovative Transformer models like BERT, GPT, XL-Net, and InstructGPT. I present the challenges we have faced in previous designs, and what the current architectures do to improve it, and upcoming challenges with Hallucination and Alignment. Sharing a link here for those interested.

attention basics bert challenges concepts embeddings explained gpt gpt4 instructgpt machinelearning nlp research seq2seq tokenization transformer transformer models video word word embeddings work