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From RNNs to Transformers to GPT-4, the leap in intelligence in Deep Learning research for Language Modelling and NLP has been a steady and educational growth. In this video, 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, as well as highlight areas of improvement for future research. For an overview of multimodal models that combine text with other input modalities like images, videos, audio, check out: The video is divided broadly into 5 chapters, each adding up to 50 concepts that chronologically cover the advancements of NLP. 0:00 - Intro 0:47 - Basics of Language Modelling 1:44 - RNNs, Seq2Seq, Encoder-Decoders 6:18 - Understanding Transformers 8:26 - LLMs - BERT, GPT, XLNet, T5 12:54 - Human Alignment, ChatGPT, GPT4 16:20 - Outro Thanks for watching! #deeplearning #machinelearning #gpt #ai Papers referenced: Word2Vec: GRUs: LSTMS: Seq2Seq: Enc-Dec Attention: Attention is All You Need: GPT: BERT: Relative Position Embeddings: Transformer-XL: T5: XL-Net: Performer: Linformer: Longfromer: LORA: GPT-3: InstructGPT: GPT-4 Technical Report:
