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- A technical report on convolution arithmetic in the context of deep learning https://github.com/vdumoulin/conv_arithmetic 6 comments
Linking pages
- GitHub - atriumlts/subpixel: subpixel: A subpixel convnet for super resolution with Tensorflow https://github.com/Tetrachrome/subpixel 44 comments
- GitHub - oprogramador/github-languages: GitHub languages statistics https://github.com/oprogramador/github-languages 5 comments
- An Introduction to different Types of Convolutions in Deep Learning | by Paul-Louis Pröve | Towards Data Science https://towardsdatascience.com/types-of-convolutions-in-deep-learning-717013397f4d 5 comments
- Making Anime Faces With StyleGAN · Gwern.net https://www.gwern.net/Faces 4 comments
- Supercharge Your Machine Learning Model Architecture with 1x1 Convolutions https://open.substack.com/pub/codecompass00/p/1x1-convolutions-supercharge--machine-learning?r=rcorn 3 comments
- GitHub - EvanLi/Github-Ranking: Github Ranking Github stars and forks ranking list. Github Top100 stars list of different languages. Automatically update daily. | Github仓库排名，每日自动更新 https://github.com/EvanLi/Github-Ranking 2 comments
- Conv3d — PyTorch 2.2 documentation https://pytorch.org/docs/stable/generated/torch.nn.Conv3d.html 2 comments
- Understanding the receptive field of deep convolutional networks | AI Summer https://theaisummer.com/receptive-field/ 1 comment
- Convolutions explained with… MS Excel! | by Thom Lane | Apache MXNet | Medium https://medium.com/apache-mxnet/convolutions-explained-with-ms-excel-465d6649831c 1 comment
- GitHub - vlgiitr/DL_Topics: List of DL topics and resources essential for cracking interviews https://github.com/vlgiitr/DL_Topics 1 comment
- Is the deconvolution layer the same as a convolutional layer? | TheAILearner https://theailearner.com/2018/12/26/is-the-deconvolution-layer-the-same-as-a-convolutional-layer/ 0 comments
- GitHub - mbadry1/Top-Deep-Learning: Top 200 deep learning Github repositories sorted by the number of stars. https://github.com/mbadry1/Top-Deep-Learning 0 comments
- Generating Letters Using Generative Adversarial Networks | by Branko Blagojevic | ml-everything | Medium https://medium.com/ml-everything/generating-letters-using-generative-adversarial-networks-gans-161b0be3c229 0 comments
- A Comprehensive Guide to Convolutional Neural Networks — the ELI5 way | by Sumit Saha | Towards Data Science https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53 0 comments
- A 2017 Guide to Semantic Segmentation with Deep Learning http://blog.qure.ai/notes/semantic-segmentation-deep-learning-review 0 comments
- Interactive demonstrations for ML courses https://arogozhnikov.github.io/2016/04/28/demonstrations-for-ml-courses.html 0 comments
- Convolutional Methods for Text. tl;dr | by Tal Perry | Medium https://medium.com/@TalPerry/convolutional-methods-for-text-d5260fd5675f 0 comments
- Using AI To Turn Your Face Into A Continuum Of Nightmares | by Nhoral | Medium https://medium.com/@nhoral/using-ai-to-turn-your-face-into-a-continuum-of-nightmares-99df713b0855 0 comments
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