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<h1><a href="index.html">labml.ai විනෝටේටඩ් පයිටෝච් කඩදාසි ක්රියාත්මක කිරීම්</a></h1>
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<p>මෙයස්නායුක ජාල සහ අදාළ ඇල්ගොරිතම සරල PyTorch ක්රියාත්මක කිරීමේ එකතුවකි. <a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations">මෙම ක්රියාත්මක කිරීම්</a> පැහැදිලි කිරීම් සමඟ ලේඛනගත කර ඇති අතර <a href="index.html">වෙබ් අඩවිය</a> මේවා පැති විසින්-ආකෘතිගත සටහන් ලෙස ඉදිරිපත් කරයි. මෙම ඇල්ගොරිතම වඩා හොඳින් තේරුම් ගැනීමට මේවා ඔබට උපකාරී වනු ඇතැයි අපි විශ්වාස කරමු. </p>
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<p>අපිමෙම අළුත්වැඩියා කිරීම සක්රියව පවත්වා ගෙන යන අතර නව ක්රියාත්මක කිරීම් එකතු කරමු. <a href="https://twitter.com/labmlai"><img alt="Twitter" src="https://img.shields.io/twitter/follow/labmlai?style=social"></a> යාවත්කාලීන කිරීම් සඳහා. </p>
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<h2>කඩදාසික්රියාත්මක කිරීම්</h2>
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<h4>✨ <a href="transformers/index.html">ට්රාන්ස්ෆෝමර්</a></h4>
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<ul><li><a href="transformers/mha.html">බහු ශීර්ෂ අවධානය</a> </li>
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<li><a href="transformers/models.html">ට්රාන්ස්ෆෝමර් ගොඩනැගිලි කොටස්</a> </li>
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<li><a href="transformers/xl/index.html">ට්රාන්ස්ෆෝමර් 40</a> </li>
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<li><a href="transformers/xl/relative_mha.html">සාපේක්ෂ බහු-ශීර්ෂ අවධානය</a> </li>
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<li><a href="transformers/rope/index.html">රොටරි ස්ථානීය කාවැද්දීම් (කඹය)</a> </li>
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<li><a href="transformers/alibi/index.html">රේඛීය පක්ෂග්රාහී (අලිබී) සමඟ අවධානය යොමු කරන්න</a> </li>
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<li><a href="transformers/retro/index.html">රෙට්රො</a> </li>
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<li><a href="transformers/compressive/index.html">සම්පීඩ්යතා ට්රාන්ස්ෆෝමර්</a> </li>
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<li><a href="transformers/gpt/index.html">Gpt ගෘහ නිර්මාණ ශිල්පය</a> </li>
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<li><a href="transformers/glu_variants/simple.html">GLU ප්රභේද</a> </li>
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<li><a href="transformers/knn/index.html">KNN-LM: කටපාඩම් කිරීම තුළින් සාමාන්යකරණය</a> </li>
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<li><a href="transformers/feedback/index.html">ප්රතිපෝෂණ ට්රාන්ස්ෆෝමර්</a> </li>
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<li><a href="transformers/switch/index.html">ට්රාන්ස්ෆෝමර් ස්විචය</a> </li>
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<li><a href="transformers/fast_weights/index.html">වේගවත් බර ට්රාන්ස්ෆෝමර්</a> </li>
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<li><a href="transformers/fnet/index.html">FNet</a> </li>
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<li><a href="transformers/aft/index.html">අවධානය නිදහස් ට්රාන්ස්ෆෝමර්</a> </li>
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<li><a href="transformers/mlm/index.html">වෙස් භාෂා ආකෘතිය</a> </li>
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<li><a href="transformers/mlp_mixer/index.html">එම්එල්පී-මික්සර්: දැක්ම සඳහා සර්ව එම්එල්පී ගෘහ නිර්මාණ ශිල්පයක්</a> </li>
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<li><a href="transformers/gmlp/index.html">MLPs (GMLP) වෙත අවධානය යොමු කරන්න</a> </li>
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<li><a href="transformers/vit/index.html">දර්ශන ට්රාන්ස්ෆෝමර් (VIT)</a> </li>
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<li><a href="transformers/primer_ez/index.html">ප්රයිමර් EZ</a> </li>
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<li><a href="transformers/hour_glass/index.html">Hourglass</a></li></ul>
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<h4>✨ <a href="recurrent_highway_networks/index.html">පුනරාවර්තන අධිවේගී මාර්ග ජාල</a></h4>
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<h4>✨ <a href="lstm/index.html">LSTM</a></h4>
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<h4>✨ <a href="hypernetworks/hyper_lstm.html">හයිපර්නෙට්වර්ක්ස් - හයිපර්එල්එස්එම්</a></h4>
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<h4>✨ <a href="resnet/index.html">රෙස්නෙට්</a></h4>
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<h4>✨ <a href="conv_mixer/index.html">කොන්වී මික්සර්</a></h4>
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<h4>✨ <a href="capsule_networks/index.html">කැප්සියුල ජාල</a></h4>
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<h4>✨ <a href="unet/index.html">යූ-නෙට්</a></h4>
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<h4>✨ <a href="gan/index.html">උත්පාදක අහිතකර ජාල</a></h4>
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<ul><li><a href="gan/original/index.html">මුල් GAN</a> </li>
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<li><a href="gan/dcgan/index.html">ගැඹුරු සංවහන ජාලයක් සහිත GAN</a> </li>
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<li><a href="gan/cycle_gan/index.html">චක්රය GAN</a> </li>
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<li><a href="gan/wasserstein/index.html">වොසර්ස්ටයින් GAN</a> </li>
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<li><a href="gan/wasserstein/gradient_penalty/index.html">ග්රේඩියන්ට් දඬුවම සමඟ වොසර්ස්ටයින් GAN</a> </li>
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<li><a href="gan/stylegan/index.html">Styleගන් 2</a></li></ul>
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<h4>✨ <a href="diffusion/index.html">විසරණ ආකෘති</a></h4>
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<ul><li><a href="diffusion/ddpm/index.html">විසරණ සම්භාවිතාව ආකෘති නිරූපණය කිරීම (DDPM)</a></li></ul>
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<h4>✨ <a href="sketch_rnn/index.html">කටු සටහනක් RNN</a></h4>
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<h4>✨ප්රස්තාරය ස්නායුක ජාල</h4>
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<ul><li><a href="graphs/gat/index.html">ප්රස්තාරය අවධානය ජාල (GAT)</a> </li>
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<li><a href="graphs/gatv2/index.html">ප්රස්තාරය අවධානය ජාල v2 (GATV2)</a></li></ul>
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<h4>✨ <a href="cfr/index.html">ප්රතිවිරුද්ධ කනගාටුව අවම කිරීම (CFR)</a></h4>
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<p>CFRසමඟ පෝකර් වැනි අසම්පූර්ණ තොරතුරු සහිත ක්රීඩා විසඳීම. </p>
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<ul><li><a href="cfr/kuhn/index.html">කුන් පෝකර්</a></li></ul>
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<h4>✨ <a href="rl/index.html">ආරකෂාවට ඉගෙනුම්</a></h4>
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<ul><li><a href="rl/ppo/index.html"><a href="rl/ppo/gae.html">සාමාන්යකරණය කළ වාසි ඇස්තමේන්තුව සමඟ ප්රොක්සිමල් ප්රතිපත්ති ප්රශස්තිකරණය</a> </a> </li>
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<li><a href="rl/dqn/model.html">ඩලිං</a> <a href="rl/dqn/index.html">නෙට්වර්ක්, <a href="rl/dqn/replay_buffer.html">ප්රමුඛතා නැවත ධාවනය</a> සහ ද්විත්ව Q ජාලය සමඟ ගැඹුරු</a> Q ජාල. </li></ul>
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<h4>✨ <a href="optimizers/index.html">ප්රශස්තකරණය</a></h4>
|
||
<ul><li><a href="optimizers/adam.html">ආදම්</a> </li>
|
||
<li><a href="optimizers/amsgrad.html">AMSGrad</a> </li>
|
||
<li><a href="optimizers/adam_warmup.html">උණුසුම් කිරීම සමඟ ආදම් ප්රශස්තකරණය</a> </li>
|
||
<li><a href="optimizers/noam.html">නව ප්රශස්තකරණය</a> </li>
|
||
<li><a href="optimizers/radam.html">නිවැරදි කරන ලද ආදම් ප්රශස්තකරණය</a> </li>
|
||
<li><a href="optimizers/ada_belief.html">ADABelief ප්රශස්තකරණය</a></li></ul>
|
||
<h4>✨ <a href="normalization/index.html">සාමාන්යකරණය ස්ථර</a></h4>
|
||
<ul><li><a href="normalization/batch_norm/index.html">කණ්ඩායම් සාමාන්යකරණය</a> </li>
|
||
<li><a href="normalization/layer_norm/index.html">ස්ථරය සාමාන්යකරණය</a> </li>
|
||
<li><a href="normalization/instance_norm/index.html">උදාහරණයක් සාමාන්යකරණය</a> </li>
|
||
<li><a href="normalization/group_norm/index.html">කණ්ඩායම් සාමාන්යකරණය</a> </li>
|
||
<li><a href="normalization/weight_standardization/index.html">බර ප්රමිතිකරණය</a> </li>
|
||
<li><a href="normalization/batch_channel_norm/index.html">කණ්ඩායම-නාලිකා සාමාන්යකරණය</a> </li>
|
||
<li><a href="normalization/deep_norm/index.html">ගැඹුරු සම්මතය</a></li></ul>
|
||
<h4>✨ <a href="distillation/index.html">ආසවනය</a></h4>
|
||
<h4>✨ <a href="adaptive_computation/index.html">අනුවර්තී ගණනය</a></h4>
|
||
<ul><li><a href="adaptive_computation/ponder_net/index.html">පොන්ඩර්නෙට්</a></li></ul>
|
||
<h4>✨ <a href="uncertainty/index.html">අවිනිශ්චිතතාව</a></h4>
|
||
<ul><li><a href="uncertainty/evidence/index.html">වර්ගීකරණ අවිනිශ්චිතතාව ප්රමාණ කිරීම සඳහා ගැඹුරු ඉගෙනීම</a></li></ul>
|
||
<h4>✨ <a href="activations/index.html">සක්රීය කිරීම්</a></h4>
|
||
<ul><li><a href="activations/fta/index.html">නොපැහැදිලි ටයිල් ක්රියාකාරකම්</a></li></ul>
|
||
<h4>✨ <a href="sampling/index.html">නියැදි ක්රම</a></h4>
|
||
<ul><li><a href="sampling/greedy.html">කෑදර නියැදීම</a> </li>
|
||
<li><a href="sampling/temperature.html">උෂ්ණත්ව නියැදීම</a> </li>
|
||
<li><a href="sampling/top_k.html">ඉහළ-K නියැදීම්</a> </li>
|
||
<li><a href="sampling/nucleus.html">න්යෂ්ටික නියැදීම</a></li></ul>
|
||
<h4>✨ <a href="neox/index.html">ජීපීටී-නියෝක්ස් එලියුටර්</a></h4>
|
||
<ul><li><a href="neox/samples/generate.html">48GB GPU මත ජනනය කරන්න</a> </li>
|
||
<li><a href="neox/samples/finetune.html">48GB GPU දෙකක් මත අවසන් කරන්න</a> </li>
|
||
<li><a href="neox/utils/llm_int8.html">LLM.INT8 ()</a></li></ul>
|
||
<h4>✨ <a href="scaling/index.html">පරිමාණ කළ හැකි පුහුණුව/අනුමානය</a></h4>
|
||
<ul><li><a href="scaling/zero3/index.html">Zero3 මතක ප්රශස්තිකරණය</a></li></ul>
|
||
<h2>උද්දීපනයකරන ලද පර්යේෂණ පත්රිකාව PDFs</h2>
|
||
<ul><li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2204.10628.pdf">Autoregressive Search Engines: Generating Substrings as Document Identifiers</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2203.15556.pdf">Training Compute-Optimal Large Language Models</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1910.02054.pdf">ZeRO: Memory Optimizations Toward Training Trillion Parameter Models</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2204.02311.pdf">PaLM: Scaling Language Modeling with Pathways</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/dall-e-2.pdf">Hierarchical Text-Conditional Image Generation with CLIP Latents</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2203.14465.pdf">STaR: Self-Taught Reasoner Bootstrapping Reasoning With Reasoning</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2112.04426.pdf">Improving language models by retrieving from trillions of tokens</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2003.08934.pdf">NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1706.03762.pdf">Attention Is All You Need</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2006.11239.pdf">Denoising Diffusion Probabilistic Models</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2109.08668.pdf">Primer: Searching for Efficient Transformers for Language Modeling</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1803.02999.pdf">On First-Order Meta-Learning Algorithms</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2103.00020.pdf">Learning Transferable Visual Models From Natural Language Supervision</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/2109.02869.pdf">The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/1805.09801.pdf">Meta-Gradient Reinforcement Learning</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/google_maps_eta.pdf">ETA Prediction with Graph Neural Networks in Google Maps</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/ponder_net.pdf">PonderNet: Learning to Ponder</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/muzero.pdf">Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/gans_n_roses.pdf">GANs N’ Roses: Stable, Controllable, Diverse Image to Image Translation (works for videos too!)</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/vit.pdf">An Image is Worth 16X16 Word: Transformers for Image Recognition at Scale</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/resnet.pdf">Deep Residual Learning for Image Recognition</a> </li>
|
||
<li><a href="https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/papers/distillation.pdf">Distilling the Knowledge in a Neural Network</a></li></ul>
|
||
<h3>ස්ථාපනය</h3>
|
||
<pre class="highlight lang-bash"><code><span></span>pip install labml-nn</code></pre>
|
||
<h3>ලැබ්එම්එල්උපුටා දක්වමින්</h3>
|
||
<p>ඔබමෙය අධ්යයන පර්යේෂණ සඳහා භාවිතා කරන්නේ නම්, කරුණාකර පහත සඳහන් බයිටෙක්ස් ප්රවේශය භාවිතා කර එය උපුටා දක්වන්න. </p>
|
||
<pre class="highlight lang-bibtex"><code><span></span><span class="nc">@misc</span><span class="p">{</span><span class="nl">labml</span><span class="p">,</span><span class="w"></span>
|
||
<span class="w"> </span><span class="na">author</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{Varuna Jayasiri, Nipun Wijerathne}</span><span class="p">,</span><span class="w"></span>
|
||
<span class="w"> </span><span class="na">title</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{labml.ai Annotated Paper Implementations}</span><span class="p">,</span><span class="w"></span>
|
||
<span class="w"> </span><span class="na">year</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{2020}</span><span class="p">,</span><span class="w"></span>
|
||
<span class="w"> </span><span class="na">url</span><span class="w"> </span><span class="p">=</span><span class="w"> </span><span class="s">{}</span><span class="p">,</span><span class="w"></span>
|
||
<span class="p">}</span><span class="w"></span></code></pre>
|
||
|
||
</div>
|
||
<div class='code'>
|
||
<div class="highlight"><pre></pre></div>
|
||
</div>
|
||
</div>
|
||
<div class='footer'>
|
||
<a href="https://papers.labml.ai">Trending Research Papers</a>
|
||
<a href="https://labml.ai">labml.ai</a>
|
||
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|
||
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