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Please ensure that any post tagged with #deeplearning is primarily on deep learning while generally can be on machine learning, data science, AI, CS, etc.

Please also be sure that your content adheres to the DEV Code of Conduct and that your comments are constructive and kind.

Conventional machine learning techniques are limited in their ability to automatically transform the raw unstructured data into a suitable internal representation. Deep learning methods are representation learning methods with multiple levels of representation, obtained by composing several simple but non-linear modules that each transform the representation at one level (starting with the raw input) into a representation at a higher, slightly more abstract level - to put it simply - Neural Networks. With the composition of enough such transformations, very complex functions can be learned - a universal approximator.
If you want to share something primarily on deep learning techniques please use #deeplearning tag.

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Oct 28

LoRA on a 16 GB card: what fits and what does not

#ai #pytorch #deeplearning #opensource

5 min read

Umesh Tharuka Malaviarachchi

Oct 28

Direct Preference Optimization: Why You Can Align a Model Without a Reward Model

#machinelearning #datascience #ai #deeplearning

8 min read

Oct 28

What EWC buys you that flat L2 does not

#ai #machinelearning #research #deeplearning

5 min read

Oct 28

Catastrophic forgetting, measured

#ai #machinelearning #research #deeplearning

5 min read

Oct 28

Blackwell quantisation on my own card

#ai #pytorch #deeplearning #research

5 min read

Umesh Tharuka Malaviarachchi

Oct 28

Mixture of Experts: Why Sparse Routing Scales Models Without Scaling Compute

#machinelearning #datascience #ai #deeplearning

7 min read

Umesh Tharuka Malaviarachchi

Oct 28

Double Descent: Why Bigger Models Can Generalise Better After Getting Worse

#machinelearning #datascience #ai #deeplearning

7 min read

Umesh Tharuka Malaviarachchi

Oct 28

Sharpness-Aware Minimization: Why Flat Minima Generalise Better Than Sharp Ones

#machinelearning #datascience #ai #deeplearning

8 min read

Viswaretas Kotra

Oct 28

How do you train a PyTorch model on multiple GPUs?

#pytorch #machinelearning #deeplearning #gpu

8 min read

Umesh Tharuka Malaviarachchi

Oct 27

Grokking: Why Neural Networks Suddenly Generalise Long After Memorising

#machinelearning #datascience #ai #deeplearning

9 min read

frank chu

Oct 27

Genie 4's real upgrade isn't the graphics. It's the second model watching them.

#ai #deeplearning #google #machinelearning

4 min read

Umesh Tharuka Malaviarachchi

Oct 27

Federated Learning: Why Averaging Model Updates Works - and When It Fails

#machinelearning #datascience #ai #deeplearning

8 min read

Felipe 0liveira

Oct 27

GRPO keeps winning the RL-vs-data argument, and LoRA gets a mechanistic audit

#ai #deeplearning #llm #machinelearning

7 min read

Umesh Tharuka Malaviarachchi

Oct 27

Graph Neural Networks: Why Message Passing Works - and Where It Breaks

#machinelearning #datascience #ai #deeplearning

9 min read

Oct 31

How much does an adapter forget?

#ai #machinelearning #research #deeplearning

5 min read

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