✨ feat adding all sources with ko folder

ko 폴더에 영어 원문 파일을 넣었습니다.
본인의 파일들을 수정해서 1~4주차를 한번에 PR 부탁드립니다

Author: movie5

.github/FUNDING.yml

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+# github: [labmlai]

.gitignore

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+.ipynb_checkpoints
+__pycache__
+.DS_Store
+.*.swp
+*.egg-info/
+dist/
+build/
+.idea/*
+.vscode/*
+!.idea/dictionaries
+labml
+labml_helpers
+labml_samples
+data
+logs
+html/
+diagrams/
+.comet.config
+settings.md
+labml_app.log

.labml.yaml

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+indicators:
+  - class_name: Scalar
+    is_print: false
+    name: param.*
+    options:
+      comet: false
+  - class_name: Scalar
+    is_print: false
+    name: grad.*
+    options:
+      comet: false
+  - class_name: Scalar
+    is_print: false
+    name: module.*
+    options:
+      comet: false
+  - class_name: Scalar
+    is_print: false
+    name: optim.*
+    options:
+      comet: false
+#web_api: http://localhost:5005/api/v1/track?

MANIFEST.in

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+include readme.rst

Makefile

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+clean: ## Clean
+	rm -rf dist
+	rm -rf build
+	rm -rf *.egg-info
+
+build: clean ## Build PIPy Package
+	python setup.py sdist bdist_wheel
+
+check-content: build  ## List contents of PIPy Package
+	tar -tvf dist/*.tar.gz
+
+check: build  ## Check PIPy Package
+	twine check dist/*
+
+upload: build  ## Upload PIPy Package
+	twine upload dist/*
+
+install:  ## Install from repo
+	pip install -e .
+
+uninstall: ## Uninstall
+	pip uninstall labml_nn
+
+docs-si: ## Sinhalese Translation
+	rm -rf docs/si
+	mv docs/zh docs_zh
+	mv docs/ja docs_ja
+	cp -r docs docs_si
+	mv docs_si docs/si
+	mv docs_zh docs/zh
+	mv docs_ja docs/ja
+	cd labml_nn; pylit --translate si --translate_cache ../translate_cache --remove_empty_sections --title_md -t ../../../pylit/templates/nn -d ../docs/si -w *
+
+docs-zh: ## Chinese Translation
+	rm -rf docs/zh
+	mv docs/si docs_si
+	mv docs/ja docs_ja
+	cp -r docs docs_zh
+	mv docs_si docs/si
+	mv docs_zh docs/zh
+	mv docs_ja docs/ja
+	cd labml_nn; pylit --translate zh --translate_cache ../translate_cache --remove_empty_sections --title_md -t ../../../pylit/templates/nn -d ../docs/zh -w *
+
+docs-ja: ## Japanese Translation
+	rm -rf docs/ja
+	mv docs/si docs_si
+	mv docs/zh docs_zh
+	cp -r docs docs_ja
+	mv docs_si docs/si
+	mv docs_zh docs/zh
+	mv docs_ja docs/ja
+	cd labml_nn; pylit --translate ja --translate_cache ../translate_cache --remove_empty_sections --title_md -t ../../../pylit/templates/nn -d ../docs/ja -w *
+
+docs: ## Render annotated HTML
+	mv docs/zh docs_zh
+	mv docs/si docs_si
+	mv docs/ja docs_ja
+	find ./docs/ -name "*.html" -type f -delete
+	find ./docs/ -name "*.svg" -type f -delete
+	mv docs_si docs/si
+	mv docs_zh docs/zh
+	mv docs_ja docs/ja
+	python utils/sitemap.py
+	python utils/diagrams.py
+	cd labml_nn; pylit --remove_empty_sections --title_md -t ../../../pylit/templates/nn -d ../docs -w *
+
+help: ## Show this help.
+	@fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//' | sed -e 's/##//'
+
+.PHONY: clean build check upload help docs
+.DEFAULT_GOAL := help

README.md

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-# labmlai_annotated_deeplearning_paper_implementation
-KOREAN Translation of labmlai/annotated_deep_learning_paper_implementations
+# 가짜연구소에서 진행하는 nn.labml.ai 페이지 번역을 위한 깃헙입니다 
+
+
+[![Twitter](https://img.shields.io/twitter/follow/labmlai?style=social)](https://twitter.com/labmlai)
+[![Sponsor](https://img.shields.io/static/v1?label=Sponsor&message=%E2%9D%A4&logo=GitHub&color=%23fe8e86)](https://github.com/sponsors/labmlai)
+
+# [labml.ai Deep Learning Paper Implementations](https://nn.labml.ai/index.html)
+
+This is a collection of simple PyTorch implementations of
+neural networks and related algorithms.
+These implementations are documented with explanations,
+
+[The website](https://nn.labml.ai/index.html)
+renders these as side-by-side formatted notes.
+We believe these would help you understand these algorithms better.
+
+![Screenshot](https://nn.labml.ai/dqn-light.png)
+
+We are actively maintaining this repo and adding new 
+implementations almost weekly.
+[![Twitter](https://img.shields.io/twitter/follow/labmlai?style=social)](https://twitter.com/labmlai) for updates.
+
+## Paper Implementations
+
+#### ✨ [Transformers](https://nn.labml.ai/transformers/index.html)
+
+* [Multi-headed attention](https://nn.labml.ai/transformers/mha.html)
+* [Transformer building blocks](https://nn.labml.ai/transformers/models.html) 
+* [Transformer XL](https://nn.labml.ai/transformers/xl/index.html)
+    * [Relative multi-headed attention](https://nn.labml.ai/transformers/xl/relative_mha.html)
+* [Rotary Positional Embeddings](https://nn.labml.ai/transformers/rope/index.html)
+* [Attention with Linear Biases (ALiBi)](https://nn.labml.ai/transformers/alibi/index.html)
+* [RETRO](https://nn.labml.ai/transformers/retro/index.html)
+* [Compressive Transformer](https://nn.labml.ai/transformers/compressive/index.html)
+* [GPT Architecture](https://nn.labml.ai/transformers/gpt/index.html)
+* [GLU Variants](https://nn.labml.ai/transformers/glu_variants/simple.html)
+* [kNN-LM: Generalization through Memorization](https://nn.labml.ai/transformers/knn)
+* [Feedback Transformer](https://nn.labml.ai/transformers/feedback/index.html)
+* [Switch Transformer](https://nn.labml.ai/transformers/switch/index.html)
+* [Fast Weights Transformer](https://nn.labml.ai/transformers/fast_weights/index.html)
+* [FNet](https://nn.labml.ai/transformers/fnet/index.html)
+* [Attention Free Transformer](https://nn.labml.ai/transformers/aft/index.html)
+* [Masked Language Model](https://nn.labml.ai/transformers/mlm/index.html)
+* [MLP-Mixer: An all-MLP Architecture for Vision](https://nn.labml.ai/transformers/mlp_mixer/index.html)
+* [Pay Attention to MLPs (gMLP)](https://nn.labml.ai/transformers/gmlp/index.html)
+* [Vision Transformer (ViT)](https://nn.labml.ai/transformers/vit/index.html)
+* [Primer EZ](https://nn.labml.ai/transformers/primer_ez/index.html)
+* [Hourglass](https://nn.labml.ai/transformers/hour_glass/index.html)
+
+#### ✨ [Eleuther GPT-NeoX](https://nn.labml.ai/neox/index.html)
+* [Generate on a 48GB GPU](https://nn.labml.ai/neox/samples/generate.html)
+* [Finetune on two 48GB GPUs](https://nn.labml.ai/neox/samples/finetune.html)
+* [LLM.int8()](https://nn.labml.ai/neox/utils/llm_int8.html)
+
+#### ✨ [Diffusion models](https://nn.labml.ai/diffusion/index.html)
+
+* [Denoising Diffusion Probabilistic Models (DDPM)](https://nn.labml.ai/diffusion/ddpm/index.html)
+* [Denoising Diffusion Implicit Models (DDIM)](https://nn.labml.ai/diffusion/stable_diffusion/sampler/ddim.html)
+* [Latent Diffusion Models](https://nn.labml.ai/diffusion/stable_diffusion/latent_diffusion.html)
+* [Stable Diffusion](https://nn.labml.ai/diffusion/stable_diffusion/index.html)
+
+#### ✨ [Generative Adversarial Networks](https://nn.labml.ai/gan/index.html)
+* [Original GAN](https://nn.labml.ai/gan/original/index.html)
+* [GAN with deep convolutional network](https://nn.labml.ai/gan/dcgan/index.html)
+* [Cycle GAN](https://nn.labml.ai/gan/cycle_gan/index.html)
+* [Wasserstein GAN](https://nn.labml.ai/gan/wasserstein/index.html)
+* [Wasserstein GAN with Gradient Penalty](https://nn.labml.ai/gan/wasserstein/gradient_penalty/index.html)
+* [StyleGAN 2](https://nn.labml.ai/gan/stylegan/index.html)
+
+#### ✨ [Recurrent Highway Networks](https://nn.labml.ai/recurrent_highway_networks/index.html)
+
+#### ✨ [LSTM](https://nn.labml.ai/lstm/index.html)
+
+#### ✨ [HyperNetworks - HyperLSTM](https://nn.labml.ai/hypernetworks/hyper_lstm.html)
+
+#### ✨ [ResNet](https://nn.labml.ai/resnet/index.html)
+
+#### ✨ [ConvMixer](https://nn.labml.ai/conv_mixer/index.html)
+
+#### ✨ [Capsule Networks](https://nn.labml.ai/capsule_networks/index.html)
+
+#### ✨ [U-Net](https://nn.labml.ai/unet/index.html)
+
+#### ✨ [Sketch RNN](https://nn.labml.ai/sketch_rnn/index.html)
+
+#### ✨ Graph Neural Networks
+
+* [Graph Attention Networks (GAT)](https://nn.labml.ai/graphs/gat/index.html)
+* [Graph Attention Networks v2 (GATv2)](https://nn.labml.ai/graphs/gatv2/index.html)
+
+#### ✨ [Counterfactual Regret Minimization (CFR)](https://nn.labml.ai/cfr/index.html)
+
+Solving games with incomplete information such as poker with CFR.
+
+* [Kuhn Poker](https://nn.labml.ai/cfr/kuhn/index.html)
+
+#### ✨ [Reinforcement Learning](https://nn.labml.ai/rl/index.html)
+* [Proximal Policy Optimization](https://nn.labml.ai/rl/ppo/index.html) with
+ [Generalized Advantage Estimation](https://nn.labml.ai/rl/ppo/gae.html)
+* [Deep Q Networks](https://nn.labml.ai/rl/dqn/index.html) with
+ with [Dueling Network](https://nn.labml.ai/rl/dqn/model.html),
+ [Prioritized Replay](https://nn.labml.ai/rl/dqn/replay_buffer.html)
+ and Double Q Network.
+
+#### ✨ [Optimizers](https://nn.labml.ai/optimizers/index.html)
+* [Adam](https://nn.labml.ai/optimizers/adam.html)
+* [AMSGrad](https://nn.labml.ai/optimizers/amsgrad.html)
+* [Adam Optimizer with warmup](https://nn.labml.ai/optimizers/adam_warmup.html)
+* [Noam Optimizer](https://nn.labml.ai/optimizers/noam.html)
+* [Rectified Adam Optimizer](https://nn.labml.ai/optimizers/radam.html)
+* [AdaBelief Optimizer](https://nn.labml.ai/optimizers/ada_belief.html)
+* [Sophia-G Optimizer](https://nn.labml.ai/optimizers/sophia.html)
+
+#### ✨ [Normalization Layers](https://nn.labml.ai/normalization/index.html)
+* [Batch Normalization](https://nn.labml.ai/normalization/batch_norm/index.html)
+* [Layer Normalization](https://nn.labml.ai/normalization/layer_norm/index.html)
+* [Instance Normalization](https://nn.labml.ai/normalization/instance_norm/index.html)
+* [Group Normalization](https://nn.labml.ai/normalization/group_norm/index.html)
+* [Weight Standardization](https://nn.labml.ai/normalization/weight_standardization/index.html)
+* [Batch-Channel Normalization](https://nn.labml.ai/normalization/batch_channel_norm/index.html)
+* [DeepNorm](https://nn.labml.ai/normalization/deep_norm/index.html)
+
+#### ✨ [Distillation](https://nn.labml.ai/distillation/index.html)
+
+#### ✨ [Adaptive Computation](https://nn.labml.ai/adaptive_computation/index.html)
+
+* [PonderNet](https://nn.labml.ai/adaptive_computation/ponder_net/index.html)
+
+#### ✨ [Uncertainty](https://nn.labml.ai/uncertainty/index.html)
+
+* [Evidential Deep Learning to Quantify Classification Uncertainty](https://nn.labml.ai/uncertainty/evidence/index.html)
+
+#### ✨ [Activations](https://nn.labml.ai/activations/index.html)
+
+* [Fuzzy Tiling Activations](https://nn.labml.ai/activations/fta/index.html)
+
+#### ✨ [Langauge Model Sampling Techniques](https://nn.labml.ai/sampling/index.html)
+* [Greedy Sampling](https://nn.labml.ai/sampling/greedy.html)
+* [Temperature Sampling](https://nn.labml.ai/sampling/temperature.html)
+* [Top-k Sampling](https://nn.labml.ai/sampling/top_k.html)
+* [Nucleus Sampling](https://nn.labml.ai/sampling/nucleus.html)
+
+#### ✨ [Scalable Training/Inference](https://nn.labml.ai/scaling/index.html)
+* [Zero3 memory optimizations](https://nn.labml.ai/scaling/zero3/index.html)
+
+## Highlighted Research Paper PDFs
+
+* [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2205.14135.pdf)
+* [Autoregressive Search Engines: Generating Substrings as Document Identifiers](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2204.10628.pdf)
+* [Training Compute-Optimal Large Language Models](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2203.15556.pdf)
+* [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1910.02054.pdf)
+* [PaLM: Scaling Language Modeling with Pathways](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2204.02311.pdf)
+* [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/dall-e-2.pdf)
+* [STaR: Self-Taught Reasoner Bootstrapping Reasoning With Reasoning](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2203.14465.pdf)
+* [Improving language models by retrieving from trillions of tokens](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2112.04426.pdf)
+* [NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2003.08934.pdf)
+* [Attention Is All You Need](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1706.03762.pdf)
+* [Denoising Diffusion Probabilistic Models](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2006.11239.pdf)
+* [Primer: Searching for Efficient Transformers for Language Modeling](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2109.08668.pdf)
+* [On First-Order Meta-Learning Algorithms](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1803.02999.pdf)
+* [Learning Transferable Visual Models From Natural Language Supervision](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2103.00020.pdf)
+* [The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2109.02869.pdf)
+* [Meta-Gradient Reinforcement Learning](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1805.09801.pdf)
+* [ETA Prediction with Graph Neural Networks in Google Maps](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/google_maps_eta.pdf)
+* [PonderNet: Learning to Ponder](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/ponder_net.pdf)
+* [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/muzero.pdf)
+* [GANs N’ Roses: Stable, Controllable, Diverse Image to Image Translation (works for videos too!)](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/gans_n_roses.pdf)
+* [An Image is Worth 16X16 Word: Transformers for Image Recognition at Scale](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/vit.pdf)
+* [Deep Residual Learning for Image Recognition](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/resnet.pdf)
+* [Distilling the Knowledge in a Neural Network](https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/distillation.pdf)
+
+### Installation
+
+```bash
+pip install labml-nn
+```
+
+### Citing
+
+If you use this for academic research, please cite it using the following BibTeX entry.
+
+```bibtex
+@misc{labml,
+ author = {Varuna Jayasiri, Nipun Wijerathne},
+ title = {labml.ai Annotated Paper Implementations},
+ year = {2020},
+ url = {https://nn.labml.ai/},
+}
+```
+
+### Other Projects
+
+#### [🚀 Trending Research Papers](https://papers.labml.ai/)
+
+This shows the most popular research papers on social media. It also aggregates links to useful resources like paper explanations videos and discussions.
+
+
+#### [🧪 labml.ai/labml](https://github.com/labmlai/labml)
+
+This is a library that let's you monitor deep learning model training and hardware usage from your mobile phone. It also comes with a bunch of other tools to help write deep learning code efficiently.
+

docs/CNAME

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+nn.labml.ai