Evolution of LLM landscape 🧵:
> RNN:
- First in line for sequential data
- problem of vanishing gradients because of long temporal dependencies
> LSTMs:
- LSTMs mitigated vanishing gradients using gating mechanisms, cell states and hidden state
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@Azaliamirh@StanfordAILab@achowdhery Thanks for creating this @Azaliamirh I have binged 3 lectures till now and completing all nine pretty soon. The ‘Large Language Monkey’ is pretty interesting!
@random_walker I was honestly thinking about this last weekend. It could save not many tokens, universal tech stacks with reverse-interpreter for human understanding.
@eyad_khrais Finally someone said it. Coming from software engineering background and working as Applied Engineer I couldn’t agree more, especially with: “software engineering trains you to think deterministically, while applied AI forces you to think probabilistically.”
@PavanKumarNY Hey @PavanKumarNY, I am currently building my mind’s replica, which will help me remember everything(inspired by LLMWiki but a lot different).
Background: incoming AI research intern at Captial One, while pursuing masters from UCSD
@endingwithali istg this has happened to me for 2 interviews. I didn’t get either of the offers but both of these HMs have pinged me on LinkedIn that they are trying to find a suitable openings because of my interview performance.
Shameless plug: actively looking for SDE/MLE summer intern
built flash mode in @TwinMind_AI's live suggestion feature, which feels like almost real-time. give it a try, link below
(ps: used a simple trick that Instagram uses for image upload)