ChatGPT - Explained!

We are going to talk about the internal workings of ChatGPT and the fundamental concepts it lies on: Language Models, Transformer Neural Networks, GPT models and Reinforcement Learning ABOUT ME ⭕ Subscribe: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 📚 Medium Blog: https://medium.com/@dataemporium 💻 Github: https://github.com/ajhalthor 👔 LinkedIn: https://www.linkedin.com/in/ajay-halthor-477974bb/ Transformer Neural Networks: https://www.youtube.com/watch?v=TQQlZhbC5ps RESOURCES [1] ChatGPT blog: https://openai.com/blog/chatgpt/ [2] Instruct GPT which is the model ChatGPT was modeled after: https://arxiv.org/pdf/2203.02155.pdf [3] Proximal Policy Optimization is how ChatGPT makes use of human rankings to update model parameters and make it more "safe" and "truthful": https://openai.com/blog/openai-baselines-ppo/ [4] Here is a paper that shows how Reinforcement learning through human feedback actually helps: https://arxiv.org/pdf/2009.01325.pdf [5] Every timestep, a subword token is generated. Here is some more information on this process with BPE: https://towardsdatascience.com/byte-pair-encoding-subword-based-tokenization-algorithm-77828a70bee0 [6] Basic Concepts in Reinforcement Learning: https://www.baeldung.com/cs/ml-policy-reinforcement-learning [7] Why Does GPT-3 write non-sensical stuff that sounds legit? https://www.alignmentforum.org/posts/BgoKdAzogxmgkuuAt/behavior-cloning-is-miscalibrated [8] What is GPT-3.5? https://beta.openai.com/docs/model-index-for-researchers MATH COURSES (7 day free trial) 📕 Mathematics for Machine Learning: https://imp.i384100.net/MathML 📕 Calculus: https://imp.i384100.net/Calculus 📕 Statistics for Data Science: https://imp.i384100.net/AdvancedStatistics 📕 Bayesian Statistics: https://imp.i384100.net/BayesianStatistics 📕 Linear Algebra: https://imp.i384100.net/LinearAlgebra 📕 Probability: https://imp.i384100.net/Probability OTHER RELATED COURSES (7 day free trial) 📕 ⭐ Deep Learning Specialization: