Like a roadmap, do A, do B And finally go through this in the end.
This was posted on HN a while ago and led to some great discussion. Myself and others agreed that this type of stateful visualization was _way_ more effective at conceptualizing how an LLM works than reading code or stepping through a debugger.
words i understand but don't really get: weights, feed forward, layers, tensors, embeddings, normalization, transformers, attention, positioning, vector
There's "programming" in the plumbing sense where you move data around through files/sockets and then there's this... somebody without a math background/education... very unlikely you'll understand it. it's just skimming python and not understand the math/library calls it makes
It helps me to think in terms of levels of abstraction rather than complexity. My education stopped at a 4 year degree, but AI is mostly postgraduate still. So I have to translate to what I know because I haven't internalized the lingo.
Here's the most approachable teaching of neural nets (NNs) and large language models (LLMs) that I've seen so far:
https://news.ycombinator.com/item?id=40213292 (Alice’s Adventures in a differentiable wonderland)
https://arxiv.org/pdf/2404.17625 (pdf)
https://news.ycombinator.com/item?id=40215592 (tensor and NN layer breadcrumbs)
II A strange land 105
7 Convolutional layers 107
..
7.1.3 Translational equivariant layers 112
..
9 Scaling up the models 143
..
9.3 Dropout and normalization 151
9.3.1 Regularization via dropout 152
9.3.2 Batch (and layer) normalization 156
III Down the rabbit-hole 167
10 Transformer models 169
10.1 Introduction 169
10.1.1 Handling long-range and sparse dependencies 170
10.1.2 The attention layer 172
10.1.3 Multi-head attention 174
10.2 Positional embeddings 177
10.2.1 Permutation equivariance of the MHA layer 177
10.2.2 Absolute positional embeddings 179
10.2.3 Relative positional embeddings 182
10.3 Building the transformer model 182
10.3.1 The transformer block and model 182
10.3.2 Class tokens and register tokens 184
11 Transformers in practice 187
11.1 Encoder-decoder transformers 187
11.1.1 Causal multi-head attention 188
11.1.2 Cross-attention 189
11.1.3 The complete encoder-decoder transformer 190
11.2 Computational considerations 191
11.2.1 Time complexity and linear-time transformers 191
11.2.2 Memory complexity and the online softmax 192
11.2.3 The KV cache 194
11.2.4 Transformers for images and audio 194
11.3 Variants of the transformer block 197That's exactly where I am at. Despite watching Karpathy's tutorial videos, I quickly got lost. My highest level of math education is Calculus 3 which I barely passed. This probably means that I will only ever understand LLMs at a high level.
Dive into Deep Learning is another.
Both have free PDF versions available.
The math isn't difficult. The notation is a little foreign, and you have to take your time reading and rereading the equations.
The only downside is that in 2024, you are probably going to use PyTorch and not Keras + Tensorflow as shown in the book.
What is the name of the course? This? https://ocw.mit.edu/courses/res-6-007-signals-and-systems-sp...
Google and find the examples where someone does it in a spreadsheet. It's much more approachable that way.
You are going to find it's not that complicated.