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> removed unflatten_index and rewriting math ops with stride-aware iteration now > this makes ops work on views (slice/transpose) without copying and replaces slow div/mod loops with cheap stride math > future ops (broadcasting, slicing, batching) will work without extra steps.
> removed unflatten_index and rewriting math ops with stride-aware iteration now > this makes ops work on views (slice/transpose) without copying and replaces slow div/mod loops with cheap stride math > future ops (broadcasting, slicing, batching) will work without extra steps. https://t.co/FWGzebnioR
shifting to linux, amazing trick
> implemented 2D matmul with correct shape logic (%) > I also have to rewrite math ops to stride-aware so that tensors created by views (slice, transpose) can share same memory without copying. If ops only use flat indexing, those views break on non-contiguous tensors.
> implemented 2D matmul with correct shape logic (%) > I also have to rewrite math ops to stride-aware so that tensors created by views (slice, transpose) can share same memory without copying. If ops only use flat indexing, those views break on non-contiguous tensors. https://t.co/Pavkh5Jho2
> added add, sub, mul, div for elementwise tensor math with full broadcasting support, so tensors of different but compatible shapes interact automatically. > added shared memory for views/reshape, letting tensors share data without copying. > framework can do basic math now.
> added add, sub, mul, div for elementwise tensor math with full broadcasting support, so tensors of different but compatible shapes interact automatically. > added shared memory for views/reshape, letting tensors share data without copying. > framework can do basic math now. https://t.co/oj0VuMlvMp
my dl framework can do this too now btw (in c++)
my dl framework can do this too now btw (in c++) https://t.co/Pngu8pfZAc
> added metadata (dtype, device, owns_data) > added reshape to create views without copying memory. > improved printing to show dtype and device. > added flat and multidimensional indexing for direct + stride based element access.
> added metadata (dtype, device, owns_data) > added reshape to create views without copying memory. > improved printing to show dtype and device. > added flat and multidimensional indexing for direct + stride based element access. https://t.co/hW8zxyUe7i
incase if anyone needs it: github.com/Mog9/micrograd…
incase if anyone needs it: github.com/Mog9/micrograd…
Implemented the first step and built the core skeleton of a tensor: > checks valid data vs shape > computes strides > track of grad storage (0 for now) > doesnt do any math yet > prints tensor in readable format.
Implemented the first step and built the core skeleton of a tensor: > checks valid data vs shape > computes strides > track of grad storage (0 for now) > doesnt do any math yet > prints tensor in readable format. https://t.co/gmwHtqs1Zt
used a different approach and it worked. the output for the equation gives correct forward and grad results. used to 2 equations and both gave correct output. the code can be made way better, but im still learning and its fine, made a working micrograd in c++
used a different approach and it worked. the output for the equation gives correct forward and grad results. used to 2 equations and both gave correct output. the code can be made way better, but im still learning and its fine, made a working micrograd in c++ https://t.co/xDRHmfrgZL
I tried to convert andrej karpathy's micrograd python code to c++ I would say 95% is working but there is still small issue where its giving wrong grad output, been on this for HOURS trying to fix
I tried to convert andrej karpathy's micrograd python code to c++ I would say 95% is working but there is still small issue where its giving wrong grad output, been on this for HOURS trying to fix https://t.co/8BoDRPsl7D
for hours thinking there is smtn wrong with my code and thats why its not giving the gradient output but my dumbass didnt even print grad, i wanna kms 😭
the knowledge u get on how autograd/backpropagation works under the hood from this lecture is insane youtube.com/watch?v=VMj-3S…
Introducing Pluto: ML tool that lets you train any dataset in minutes right in your browser, with no coding required. Just upload a dataset, pick a target column, and run multiple models in one go, get results right away with plots. more models, hyper-parameters coming soon.
only few chapters left from learncpp, done with most of the concepts. need to understand/focus on few things before starting my project next week, gn.
🧵Thread on How Activation Functions Power Neural Networks: The core of what makes neural networks powerful we'll break it down step by step: why stacking only linear layers fails, how activation functions add non-linearity, how that changes what a network can learn, and more.
both are very outdated, it can be made for very simple answers as it can't handle complex things, and gpt2 has a max context window of 1024 tokens so it would forget earlier convos and get messy.
both are very outdated, it can be made for very simple answers as it can't handle complex things, and gpt2 has a max context window of 1024 tokens so it would forget earlier convos and get messy.
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