Github Giassuncao Deep Learning Optimizers A Repository To Organize
A repository to make available and organize the codes developed during the execution of a technical note on Medium about Optimization in Deep Learning. These codes enable practical visualization of the theoretical concepts covered in the work, this is part of the coursework for the Machine Learning course by professor Ivanovitch Medeiros. The code in the .ipynb files can be found under 'files' in this repository or accessed directly through these Google Colab links: 1. Visualizando Gradientes Adaptados: Code to help visualize the changes in gradients, corrected gradients, and adapted gradients throughout model training, using EWMA and the Adam optimizer. 2.
SGD Momentum e Nesterov: Code to help compare the behavior of SGD optimizer in three ways: normal, with momentum, and with Nesterov momentum. Analyzing gradients, path and loss functions. 3. Learning Rate Schedulers: Code to help understand the differences in a model training using learning rate schedulers, specifically StepLR and CyclicLR. There was an error while loading. Please reload this page.
A repository to make available and organize the codes developed during the execution of a technical note on Medium about Optimization in Deep Learning. These codes enable practical visualization of the theoretical concepts covered in the work, this is part of the coursework for the Machine Learning course by professor Ivanovitch Medeiros. The code in the .ipynb files can be found under 'files' in this repository or accessed directly through these Google Colab links: 1. Visualizando Gradientes Adaptados: Code to help visualize the changes in gradients, corrected gradients, and adapted gradients throughout model training, using EWMA and the Adam optimizer. 2.
SGD Momentum e Nesterov: Code to help compare the behavior of SGD optimizer in three ways: normal, with momentum, and with Nesterov momentum. Analyzing gradients, path and loss functions. 3. Learning Rate Schedulers: Code to help understand the differences in a model training using learning rate schedulers, specifically StepLR and CyclicLR. This repository contains the results for the paper: "Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers" Semester project on the impact of label noise on deep learning optimization
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�?&��h�%���B�u��g����i�a-Ǎa6v��`L�_Xn�b��n=�9Z91N0�s��L��HHX��ȭWs���[F��[1�$u�e� ,9m ,Lt5f��\�Bw+���q���5���8T[H�kX����F��(x�tvjqH{�IE-�Īc��MVn��_���� ���e)��bT����C�$hX��DL�Ix������kc��3���`'�S_Ǹ���B�,z�%���E^��r�W�V�Qj����ؘ,F�þ�B�=h��#("Տ�� _ ������V���&�Q*�`��e��O������� fx��� ��t��ct7�.k"�Gi�m�2��놇�v�QB6�댈�0�@{� �����ȇ�a�� �v f ��^�����L�/$G��*(,�*M�K�9�����s4o8�pC��q���6w�O��^��Es�\��k~�j����s�H�\����oii�����bp�L18���h�gɴRw�� f�LKm���;-�҆�Դ�e�,�Gb8�J6�ɜl2�edP�q�Hq�Wqby��v�Ls�'��g3F�SdCf�M� ��H���S�ۘ�p����S��+�}�?��R����u/��{�� �..�<aG��uc� �-E7�|x�j%���.|�8D��J���p�Ag��h�v��q��[j���W/�3�I������h�̅5�k���ƽyu'���6P"�yb�q�L?(�Q#�K���ݜM� g��2�,��!n�p�rj(�$��-L���\�Hg�j��CGy?�yֽ�����tK@�)P��O�d4� ]��EC�� ��D�/ ������27KgC�]ׯ��S�`�N(�Gc�����+�C�Ĩ���X�BO��'�� &�~+H/)�\ۘ��f[�l�̎���y��=>7�wpυ�}x ��e���Z� ���$��^��X�U����YW�Ł�'�|�����yt�)�&y���( �c���n����t%]!9bKvF~����+8)t��M'�]]��j�����w��h�>�]�(�M*_6�� �͆��JoPexL�y�l�C&k�0h�(��v]��8�uf���a2)'�8�6qb �9��/�Y��v�2�Լ��*��=�����-X��&�Ɲ�ZR�䎒�o�f.�����Y���V:s�?��u䞹E�W��,紆9Β͏�__ ���ð�TrQ�gbr��IL���ۣ �� ��t�M���]�ǁY��W���-�ˠ����Ҳ�R��p�=�d��߶�u��_�j,���w�Y�������ssW�W�O����h�n(�w�ȁ���?wZ���`ފ��m��[�T���8����\���q쌗����l�,s_���u�]�_�y�����r�Y� K��6�� �s��/?��n�_ח��*�u%$5�rX Q��(3 �F�{��eR8`��v�0�kR�y���7>}�;/�d�3�=�渾���]��w������k��+~�QN��='��_]��֟������������>dG�,�-+�DLO����2%^�-f�����`�����(�U�l�Ïӭl0� v!��+\�XK'U�'����/&M����T���q�'��66����]���ِ9!����%>������G[��)���8w����{���uo�,���i!uR�'Ւ�:aQfl�<��... Ma�$��I���ba���O���۶hh�٘ӿc�{4!�Ƣ�i�$�&��"7:�]b�Ūޙ���ϩ��K�r㡶PA�S�鐏�/�K�c�آ����8N�7q};5FX�k��|��X�j�dD Ӌ�Y"�m�gaK�,�-�����|������^���+}y�������J��))[�ə�`�;�s�[/�W����]��ӽ|S�����g���`/#�Yԇ�fd$� B��.�g0>�#bq�d/�YQ�*3��9�"��Z��eo�X�d��3+�;�Z��9����m�,L�[Y�xV.B�,7/��5����e�~WC��� C'�7�`��7�Yߗ���� <�<�2U�gB�<����i6 J�e7�>��KkR���+��O?�eN�$�?)�ܼ��YO� �c �J�u�E�W�kz��4wO�ӯ�w=>����nTY��/�� ���b��>o��o2��&g�)�uɸ �dD��}ƽן'��\�r�uI��.Ѹ�� ^�s�ś��@i�3���� ����[4$�j>�^��RV��8L|�z��l�{:�cX_ 8+����?���E�2@+[���3" ��à�.��N� *ߧ-R?p+`�H;�?H%�>r��B��`�A%�h*��e�"���\��C�K�h�8J��L�R��;�%�A}�?��-t�;����]p���0�-��6�Ur���j㯩�Y�+^"�g���Go*����Sl��2ۃs��"�Q�։i�+/������� G��/���׀i��d�Bvtr�xH������u�3���#������=�=�N Ԩ��HC�a�} ���T�@��vpG�"���FDZ��t�`���N��br����B��8��0�+GD��z���b�6f��l�k1l��'�59�o���_=�頄�����6���#�S �+��4��fX �/lfX �a�:�[�t � �f:�\oņ�b����t��t��d~�x�|~�ǀ�V���3_�����q���݊�w�h�ۻ}3{g6��U��0o¿�\�O� xy����c��_�����X��:�~�*MR��.JeT/o�z �z�%J�G��������)��t-}J[�.5�]�����R/i��m�9ѫ^brbw�wA��m�����_����l�S��&m� �ģ���b� g0��M����ʰzT�^̵k��.^U_��~ ����I����ɛ�����a>��>��;=�ݓ��s,n�N ϫ����5�C߸�&����HzU��Β?Uw���"r�i�`��`8�Dd��1�̈��w���F�F*12���3�2Z��hb4�i�c@|����{Q�;�S���(m,�<y}����(w���'� �Z~� ���������7tP��C����>�W�SX�ȿ����i������x8N��o"q.!����8��*�揓Vq��Y%�nTDX�̀�O&im�~x�:��1}Fb\=�o��Sn� ���m ۦ��u�:�%ɴM|�* i�G��p�Qu���... Welcome to the dynamic world of deep learning! Today, we will explore the hottest trending GitHub repositories in deep learning, an essential resource for anyone keen on enhancing their AI toolkit. Our list showcases the top 100 deep learning repositories ordered by the number of stars gained recently. Let’s dive into understanding this exciting landscape!
GitHub serves as a vibrant hub where developers share their projects, fostering a collaborative environment for learning and innovation. For those new to deep learning, GitHub is like a treasure chart, mapping out various valuable tools and resources. The trending repositories were identified using GitHub’s search API, focusing on keywords like “deep-learning,” “CNN,” “RNN,” and more. Repositories boasting 40,000 stars or more were judiciously excluded to keep our list fresh and relevant. Think of each repository as an essential ingredient in a chef’s kitchen. While some tools might focus on recipe creation (like spinningup), others refine the cooking process itself (like Real-Time-Voice-Cloning).
Just as a skilled chef adapts their tools to create culinary masterpieces, AI developers utilize these resources to build sophisticated models that can accomplish astounding tasks. Here are a few troubleshooting ideas to guide you on your journey through these repositories: There was an error while loading. Please reload this page. Automate your workflow from idea to production GitHub Actions makes it easy to automate all your software workflows, now with world-class CI/CD.
Build, test, and deploy your code right from GitHub. Hosted runners for every major OS make it easy to build and test all your projects. Run directly on a VM or inside a container. Use your own VMs, in the cloud or on-prem, with self-hosted runners. Save time with matrix workflows that simultaneously test across multiple operating systems and versions of your runtime.
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A Repository To Make Available And Organize The Codes Developed
A repository to make available and organize the codes developed during the execution of a technical note on Medium about Optimization in Deep Learning. These codes enable practical visualization of the theoretical concepts covered in the work, this is part of the coursework for the Machine Learning course by professor Ivanovitch Medeiros. The code in the .ipynb files can be found under 'files' in ...
SGD Momentum E Nesterov: Code To Help Compare The Behavior
SGD Momentum e Nesterov: Code to help compare the behavior of SGD optimizer in three ways: normal, with momentum, and with Nesterov momentum. Analyzing gradients, path and loss functions. 3. Learning Rate Schedulers: Code to help understand the differences in a model training using learning rate schedulers, specifically StepLR and CyclicLR. There was an error while loading. Please reload this page...
A Repository To Make Available And Organize The Codes Developed
A repository to make available and organize the codes developed during the execution of a technical note on Medium about Optimization in Deep Learning. These codes enable practical visualization of the theoretical concepts covered in the work, this is part of the coursework for the Machine Learning course by professor Ivanovitch Medeiros. The code in the .ipynb files can be found under 'files' in ...
SGD Momentum E Nesterov: Code To Help Compare The Behavior
SGD Momentum e Nesterov: Code to help compare the behavior of SGD optimizer in three ways: normal, with momentum, and with Nesterov momentum. Analyzing gradients, path and loss functions. 3. Learning Rate Schedulers: Code to help understand the differences in a model training using learning rate schedulers, specifically StepLR and CyclicLR. This repository contains the results for the paper: "Desc...
Guava Disease Classification Using DenseNet-Reloaded And ADOPT Optimizer. Achieves 99.73%
Guava Disease Classification using DenseNet-Reloaded and ADOPT optimizer. Achieves 99.73% accuracy in Anthracnose, Fruit Fly, and Healthy Guava detection. Toy implementations of popular deep learning optimizers from scratch in JAX Instantly share code, notes, and snippets. Explore AI Frontiers, Master Industry Trends Your Daily AI Brief - Never Miss What's Next