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I trained the model on the ",{"type":19,"tag":33,"props":1748,"children":1751},{"href":1749,"rel":1750},"https://www.tensorflow.org/datasets/catalog/tiny_shakespeare",[37],[1752],{"type":25,"value":1753},"tiny Shakespeare dataset",{"type":25,"value":1755},"\nand used it to generate text in the style of Shakespeare.",{"type":19,"tag":27,"props":1757,"children":1758},{},[1759],{"type":25,"value":1760},"Transformers are a type of neural network architecture that employes techniques such as:",{"type":19,"tag":158,"props":1762,"children":1763},{},[1764,1776,1788,1799],{"type":19,"tag":162,"props":1765,"children":1766},{},[1767,1774],{"type":19,"tag":33,"props":1768,"children":1771},{"href":1769,"rel":1770},"https://towardsdatascience.com/illustrated-self-attention-2d627e33b20a",[37],[1772],{"type":25,"value":1773},"Self-attention",{"type":25,"value":1775},", which allows the model to learn the relationships\nbetween different parts of the input data.",{"type":19,"tag":162,"props":1777,"children":1778},{},[1779,1786],{"type":19,"tag":33,"props":1780,"children":1783},{"href":1781,"rel":1782},"https://kazemnejad.com/blog/transformer_architecture_positional_encoding/",[37],[1784],{"type":25,"value":1785},"Positional encoding",{"type":25,"value":1787},", which allows the model to learn the\nrelative positions of the input data.",{"type":19,"tag":162,"props":1789,"children":1790},{},[1791,1797],{"type":19,"tag":33,"props":1792,"children":1794},{"href":1769,"rel":1793},[37],[1795],{"type":25,"value":1796},"Multi-head attention",{"type":25,"value":1798},", which allows the model to learn\ndifferent relationships between different parts of the input data.",{"type":19,"tag":162,"props":1800,"children":1801},{},[1802,1808],{"type":19,"tag":33,"props":1803,"children":1805},{"href":1769,"rel":1804},[37],[1806],{"type":25,"value":1807},"Residual connections",{"type":25,"value":1809},", which allows the model to learn\nthe difference between the input data and the output data.",{"title":12,"searchDepth":656,"depth":656,"links":1811},[],"content:projects:deep-learning:transfusion.md","projects/deep-learning/transfusion.md",{"_path":1815,"_dir":1723,"_draft":11,"_partial":11,"_locale":12,"title":1816,"description":1817,"order":1400,"month":1401,"year":1570,"date":1818,"repo":1819,"tech":1820,"featured":1409,"navigation":11,"tag":49,"body":1821,"_type":658,"_id":1877,"_source":660,"_file":1878,"_extension":662},"/projects/deep-learning/adversarial","Adversarial Training for Neural Networks","Experimentation with various adversarial training techniques for neural networks.\nAdversarial training is useful to improve the robustness of neural networks to\nadversarial attacks — which often happen as noise in the input data.\nTechniques explored include:","2023-05-17T00:00:00.000Z","https://github.com/siavava/deep-learning/tree/main/homeworks/02",[1406,1729,1730],{"type":16,"children":1822,"toc":1875},[1823,1827],{"type":19,"tag":27,"props":1824,"children":1825},{},[1826],{"type":25,"value":1817},{"type":19,"tag":158,"props":1828,"children":1829},{},[1830,1851,1863],{"type":19,"tag":162,"props":1831,"children":1832},{},[1833,1840,1842,1849],{"type":19,"tag":33,"props":1834,"children":1837},{"href":1835,"rel":1836},"https://www.datacamp.com/tutorial/complete-guide-data-augmentation",[37],[1838],{"type":25,"value":1839},"Data augmentation",{"type":25,"value":1841},", which helps the model have more data to learn from.\nNew samples are generated by randomly cropping and/or flipping some images.\nWe also add ",{"type":19,"tag":33,"props":1843,"children":1846},{"href":1844,"rel":1845},"https://www.sciencedirect.com/science/article/pii/S0167865521002440",[37],[1847],{"type":25,"value":1848},"pertubations",{"type":25,"value":1850},", to a random subset of images,\nwhich helps the model learn to be robust to noise.",{"type":19,"tag":162,"props":1852,"children":1853},{},[1854,1861],{"type":19,"tag":33,"props":1855,"children":1858},{"href":1856,"rel":1857},"https://paperswithcode.com/method/dropout",[37],[1859],{"type":25,"value":1860},"Dropout",{"type":25,"value":1862},", which entails randomly blocking a subset of the neurons in the network\nfrom transmitting information during training. 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