Post by Timur Bikmukhametov, PhD

12 papers you must read as a Data Scientist 👇 --- 👉 𝗪𝗮𝗻𝗻𝗮 𝗹𝗲𝗮𝗿𝗻 𝗙𝗢𝗥 𝗙𝗥𝗘𝗘 𝗵𝗼𝘄 𝘁𝗼 𝗺𝗼𝗱𝗲𝗹 & 𝗱𝗲𝘀𝗶𝗴𝗻 𝗠𝗟 𝗦𝘆𝘀𝘁𝗲𝗺𝘀? My FREE course: https://lnkd.in/esDTaubE --- 𝟭𝟮. "𝗥𝗮𝗻𝗱𝗼𝗺 𝗙𝗼𝗿𝗲𝘀𝘁𝘀" by Leo Breiman (2001) Introduced Random Forests, a powerful ensemble learning method. 𝟭𝟭. "𝗟𝗮𝘁𝗲𝗻𝘁 𝗗𝗶𝗿𝗶𝗰𝗵𝗹𝗲𝘁 𝗔𝗹𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻" by David M. Blei et al (2003) Presented a generative probabilistic model for topic modeling. 𝟭𝟬. "𝗚𝗿𝗲𝗲𝗱𝘆 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻 𝗔𝗽𝗽𝗿𝗼𝘅𝗶𝗺𝗮𝘁𝗶𝗼𝗻: 𝗔 𝗚𝗿𝗮𝗱𝗶𝗲𝗻𝘁 𝗕𝗼𝗼𝘀𝘁𝗶𝗻𝗴 𝗠𝗮𝗰𝗵𝗶𝗻𝗲" by Jerome Friedman (2001) Introduced Gradient Boosting Machines, a foundational method in ensemble learning. 𝟵. "𝗔 𝗙𝗲𝘄 𝗨𝘀𝗲𝗳𝘂𝗹 𝗧𝗵𝗶𝗻𝗴𝘀 𝘁𝗼 𝗞𝗻𝗼𝘄 𝗔𝗯𝗼𝘂𝘁 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴" by Pedro Domingos (2012) Practical insights into machine learning concepts and challenges. 𝟴. "𝗦𝘂𝗽𝗽𝗼𝗿𝘁-𝗩𝗲𝗰𝘁𝗼𝗿 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀" 𝗯𝘆 𝗖𝗼𝗿𝘁𝗲𝘀 𝗮𝗻𝗱 𝗩𝗮𝗽𝗻𝗶𝗸 (𝟭𝟵𝟵𝟱) This groundbreaking paper introduced Support Vector Machines (SVMs), a powerful supervised learning algorithm for classification and regression tasks. 𝟳. "𝗔𝗹𝗲𝘅𝗡𝗲𝘁: 𝗜𝗺𝗮𝗴𝗲𝗡𝗲𝘁 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝗗𝗲𝗲𝗽 𝗖𝗼𝗻𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗹 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀" by Krizhevsky et al. (2012) Revolutionized image classification using CNNs. 𝟲. "𝗗𝗿𝗼𝗽𝗼𝘂𝘁: 𝗔 𝗦𝗶𝗺𝗽𝗹𝗲 𝗪𝗮𝘆 𝘁𝗼 𝗣𝗿𝗲𝘃𝗲𝗻𝘁 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 𝗳𝗿𝗼𝗺 𝗢𝘃𝗲𝗿𝗳𝗶𝘁𝘁𝗶𝗻𝗴" by Srivastava et al. (2014) Proposed dropout as a regularization method for neural networks. 𝟱. "𝗕𝗮𝘁𝗰𝗵 𝗡𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: 𝗔𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗗𝗲𝗲𝗽 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗯𝘆 𝗥𝗲𝗱𝘂𝗰𝗶𝗻𝗴 𝗜𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗖𝗼𝘃𝗮𝗿𝗶𝗮𝘁𝗲 𝗦𝗵𝗶𝗳𝘁" by Sergey Ioffe and Christian Szegedy (2015) Improved training speed and stability of deep networks. 𝟰. "𝗗𝗲𝗲𝗽 𝗥𝗲𝘀𝗶𝗱𝘂𝗮𝗹 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗜𝗺𝗮𝗴𝗲 𝗥𝗲𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻 (𝗥𝗲𝘀𝗡𝗲𝘁)" by He et al. (2016) Introduced residual networks that enable very deep architectures. 𝟯. "𝗔𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝗜𝘀 𝗔𝗹𝗹 𝗬𝗼𝘂 𝗡𝗲𝗲𝗱 (𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀)" by Vaswani et al. (2017) Revolutionized NLP with transformer architectures, forming the basis of GPT and BERT models. 𝟮. "𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗱𝘃𝗲𝗿𝘀𝗮𝗿𝗶𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (𝗚𝗔𝗡𝘀)" by Goodfellow et al. (2014) Proposed GANs for generative modeling tasks in deep learning applications. 𝟭. "𝗦𝗲𝗾𝘂𝗲𝗻𝗰𝗲 𝘁𝗼 𝗦𝗲𝗾𝘂𝗲𝗻𝗰𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀" by Sutskever et al. (2014) Introduced seq-to-seq models for machine translation tasks using deep learning techniques. --- 👉 𝗪𝗮𝗻𝗻𝗮 𝗹𝗲𝗮𝗿𝗻 𝗙𝗢𝗥 𝗙𝗥𝗘𝗘 𝗵𝗼𝘄 𝘁𝗼 𝗺𝗼𝗱𝗲𝗹 & 𝗱𝗲𝘀𝗶𝗴𝗻 𝗠𝗟 𝗦𝘆𝘀𝘁𝗲𝗺𝘀? My FREE course: https://lnkd.in/esDTaubE