Looking at what machine learning is, what neural networks are, what GPT is and then how that all plays into ChatGPT!
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▬▬▬▬▬▬ C H A P T E R S ⏰ ▬▬▬▬▬▬
0:00 – Introduction
4:29 – History and training
8:55 – Machine learning
12:47 – Prediction
14:32 – More complex predictions
17:30 – Making any shape with enough curves combined
24:30 – How to get different curves from one curve
28:23 – Weights and bias
33:33 – Activation functions
40:02 – Back propagation and gradient descent
50:48 – Visualization of layers working together
53:30 – What is GPT
57:39 – Attention
1:00:00 – Tokens
1:02:33 – GPT-2
1:07:22 – GPT-3
1:09:10 – zero, one and few shot
1:10:19 – What was it trained on
1:13:45 – ChatGPT
1:18:40 – Closing thoughts
▬▬▬▬▬▬ K E Y L I N K S 🔗 ▬▬▬▬▬▬
► Whiteboard:
🔗 https://github.com/johnthebrit/RandomStuff/raw/master/Whiteboards/WhatisChatGPT.png
► ChatGPT site at OpenAI:
🔗 https://chat.openai.com/chat
► Great example video of number recognition:
🔗 https://youtu.be/Tsvxx-GGlTg
► OpenAI tokenizer:
🔗 https://platform.openai.com/tokenizer
► GPT 2 paper:
🔗 https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
► GPT-3 paper:
🔗 https://arxiv.org/pdf/2005.14165.pdf
► ChatGPT overview:
🔗 https://openai.com/blog/chatgpt
▬▬▬▬▬▬ Want to learn more? 🚀 ▬▬▬▬▬▬
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