Gen 2 canonical — 20 files
19
DNN Math Cheatsheet
linear algebra / activation / loss / backprop / transformer
→
20
CUDA + Tensor Cores
gpu architecture / warps / memory hierarchy / mixed precision
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18
Activation Functions
sigmoid / relu / gelu / vanishing gradient
→
01
Attention Mechanism
transformers / qkv / multi-head / flash attn
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02
Attention Mechanism II
transformers / query-key-value / extended
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03
Backprop vs STDP
learning rules / bio-plausibility
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04
Backpropagation
gradient descent / chain rule
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05
Convolutional Networks
kernels / feature maps / receptive field
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06
Connectome
network topology / graph structure
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07
Diffusion Models
ddpm / score matching / latent diffusion
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08
Embedding Geometry
vector space / superposition / homology
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09
Embeddings + Vector Space
semantic geometry / dimensionality
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10
Hopfield Networks
energy landscapes / associative memory / attention
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11
LoRA Explainer
fine-tuning / low-rank adaptation
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12
Neuron Comparison
bio vs artificial / morphology
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13
Perceptron Walkthrough
code / step-by-step
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14
Perceptron Viz
interactive / weights + bias
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16
Mamba / SSM
state space models / selective scan / hippo
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17
Prompt Engineering
asking nicely / vibes / linkedin certified
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15
Reservoir Computing
echo state networks / dynamics
→