PyTorch Tutorial: nn.RNNCell скачать в хорошем качестве

PyTorch Tutorial: nn.RNNCell

PyTorch RNNCell: Building Blocks of Recurrent Neural Networks In this comprehensive lecture, we dive deep into PyTorch's RNNCell, the fundamental building block of Recurrent Neural Networks. Learn how to implement, customize, and optimize RNNCell for various sequence processing tasks. 📚 What you'll learn: • Understanding RNNCell architecture and parameters • Mathematical foundations and gradient flow • Practical implementation techniques • Performance optimization strategies • Common pitfalls and best practices ⏱️ Timestamps: 00:00 PyTorch RNNCell: Building Blocks of Recurrent Neural Networks 00:48 RNNCell Architecture Overview 01:14 Mathematical Foundation 01:49 Simplest RNNCell Example 02:34 RNNCell Parameters Documentation 03:03 Exploring Weight Matrices and Biases 04:11 Effect of Bias Parameter 04:53 Nonlinearity Options: tanh vs ReLU 06:04 Sequential Processing with RNNCell 07:29 Manual Computation Verification 08:20 Gradient Flow and Backpropagation 09:28 Building a Custom RNN Layer using RNNCell 10:10 Practical Application: Sequence Generation 11:16 Advanced: Bidirectional Processing with RNNCell 12:06 Common Pitfalls and Best Practices 12:51 Performance Comparison: tanh vs ReLU 13:57 Summary and Best Practices