معلومات عن هذه الحلقة
<p>Deep Learning - Regularization Part 3</p>
<p>This video discusses normalization such as batch normalization and self normalizing units and explains the concepts of drop-out and drop-connect</p>
<p>Video References:<br />
<a href="https://www.youtube.com/channel/UCSHZKyawb77ixDdsGog4iWA">Lex Fridman's Channel</a></p>
<p>Further Reading:<br />
<a href="https://www.sciencedirect.com/science/article/pii/S093938891830120X">A gentle Introduction to Deep Learning</a></p>
<p>This video discusses normalization such as batch normalization and self normalizing units and explains the concepts of drop-out and drop-connect</p>
<p>Video References:<br />
<a href="https://www.youtube.com/channel/UCSHZKyawb77ixDdsGog4iWA">Lex Fridman's Channel</a></p>
<p>Further Reading:<br />
<a href="https://www.sciencedirect.com/science/article/pii/S093938891830120X">A gentle Introduction to Deep Learning</a></p>
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البحث في الحلقات الماضية
البحث في الحلقات السابقة من Deep Learning.
حلقات أخرى في هذا البودكاست
<p>Deep Learning - Regularization Part 4</p>
<p>This video discusses initialization techniques and transfer learning.</p>
<p>Video References:<br />
<a href="https://www.youtube.com/channel/UCSHZKyawb77ixDdsGog4iWA">Lex Fridman's Channel</a>…
<p>Deep Learning - Regularization Part 5</p>
<p>This video discusses multi-task learning.</p>
<p>Video References:<br />
<a href="https://www.youtube.com/channel/UCSHZKyawb77ixDdsGog4iWA">Lex Fridman's Channel</a></p>
<p>Further…
<p>Deep Learning - Regularization Part 2</p>
<p>This video discusses classical regularization techniques such as early stopping using a validation set, augmentation, and maximum a-posteriori methods that expand the loss function using a regularization term.</p>
<p>Vi…
<p>Deep Learning - Common Practices Part 1</p>
<p>This video discusses the use of validation data and how to choose optimizers, monitor weights, and set learning rates including their annealing.</p>
<p>Video References:<br />
<a href="https://www.youtube.com…
إخلاء المسؤولية: البودكاست والأعمال الفنية المضمنة في هذه الصفحة مأخوذة من Prof. Dr.-Ing. Andreas Maier، وهي مملوكة لمالكها وليست تابعة لشركة Listen Notes, Inc أو معتمدة منها.
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