Complete PyTorch course: Add comprehensive recommended reads sections to all 20 chapters

Author: shivamkumar-kimbal

docs/courses/pytorch/13-rnns.md

@@ -640,8 +640,33 @@ Continue to [Chapter 14: Transfer Learning](14-transfer-learning.md) for:
 - ✅ Attention mechanism improves long sequences
 - ✅ Teacher forcing speeds up training
 
----
+## Recommended Reads
+
+???+ "📚 Official Documentation"
+    1. [RNN](https://pytorch.org/docs/stable/generated/torch.nn.RNN.html) - RNN layer documentation
+    2. [LSTM](https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html) - LSTM layer documentation
+    3. [GRU](https://pytorch.org/docs/stable/generated/torch.nn.GRU.html) - GRU layer documentation
+    4. [Packed Sequences](https://pytorch.org/docs/stable/generated/torch.nn.utils.rnn.pack_padded_sequence.html) - Handling variable-length sequences
+
+???+ "📖 Essential Articles"
+    1. [RNN Tutorial](https://pytorch.org/tutorials/intermediate/char_rnn_classification_tutorial.html) - Building RNNs
+    2. [LSTM for Time Series](https://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html) - Time series forecasting
+    3. [Sequence-to-Sequence Models](https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html) - Seq2Seq tutorial
+    4. [Attention Mechanisms](https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html) - Attention in RNNs
+
+???+ "🎓 Learning Resources"
+    1. [NLP with PyTorch](https://pytorch.org/tutorials/beginner/nlp/deep_learning_tutorial.html) - NLP tutorial
+    2. [RNN Best Practices](https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html) - Training tips
+    3. [Gradient Clipping](https://pytorch.org/docs/stable/generated/torch.nn.utils.clip_grad_norm_.html) - Preventing gradient explosion
+
+???+ "💡 Best Practices"
+    1. [Handling Variable-Length Sequences](https://pytorch.org/docs/stable/generated/torch.nn.utils.rnn.pack_padded_sequence.html) - Packed sequences
+    2. [Teacher Forcing](https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html) - Training strategies
+    3. [Bidirectional RNNs](https://pytorch.org/docs/stable/generated/torch.nn.RNN.html) - Bidirectional processing
+
+???+ "🔬 Research Papers"
+    1. [Long Short-Term Memory](https://www.bioinf.jku.at/publications/older/2604.pdf) - LSTM paper
+    2. [Learning Phrase Representations using RNN Encoder-Decoder](https://arxiv.org/abs/1406.1078) - GRU paper
+    3. [Attention Is All You Need](https://arxiv.org/abs/1706.03762) - Transformer architecture
 
-**Reference:**
-- [RNN Documentation](https://pytorch.org/docs/stable/generated/torch.nn.RNN.html)
-- [LSTM Documentation](https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html)
+---

docs/courses/pytorch/14-transfer-learning.md

@@ -558,8 +558,33 @@ Continue to [Chapter 15: Model Saving & Loading](15-model-saving.md) to learn ab
 - ✅ Use different learning rates for different parts
 - ✅ Always normalize inputs correctly for pre-trained models
 
----
+## Recommended Reads
+
+???+ "📚 Official Documentation"
+    1. [Transfer Learning Tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) - Complete transfer learning guide
+    2. [torchvision.models](https://pytorch.org/vision/stable/models.html) - Pre-trained models
+    3. [Model Zoo](https://pytorch.org/vision/stable/models.html) - Available models
+    4. [Fine-Tuning Guide](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) - Fine-tuning strategies
+
+???+ "📖 Essential Articles"
+    1. [Transfer Learning Explained](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) - Transfer learning concepts
+    2. [Feature Extraction vs Fine-Tuning](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) - When to use each
+    3. [Progressive Unfreezing](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) - Advanced techniques
+    4. [Domain Adaptation](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) - Adapting to new domains
+
+???+ "🎓 Learning Resources"
+    1. [Computer Vision Transfer Learning](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) - CV examples
+    2. [NLP Transfer Learning](https://huggingface.co/transformers/) - Hugging Face transformers
+    3. [Transfer Learning Best Practices](https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html) - Optimization tips
+
+???+ "💡 Best Practices"
+    1. [Learning Rate Selection](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) - Choosing LRs for fine-tuning
+    2. [Freezing Layers](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) - When to freeze
+    3. [Data Augmentation](https://pytorch.org/vision/stable/transforms.html) - Augmentation for transfer learning
+
+???+ "🔬 Research Papers"
+    1. [How transferable are features in deep neural networks?](https://arxiv.org/abs/1411.1792) - Transfer learning analysis
+    2. [Rethinking ImageNet Pre-training](https://arxiv.org/abs/1811.08883) - Pre-training effectiveness
+    3. [BERT: Pre-training of Deep Bidirectional Transformers](https://arxiv.org/abs/1810.04805) - NLP transfer learning
 
-**Reference:**
-- [Transfer Learning Tutorial](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html)
-- [torchvision.models](https://pytorch.org/vision/stable/models.html)
+---

docs/courses/pytorch/15-model-saving.md

@@ -638,8 +638,32 @@ Continue to [Chapter 20: Best Practices](20-best-practices.md) for:
 - ✅ Implement checkpoint management early
 - ✅ Save best model separately from checkpoints
 
----
+## Recommended Reads
+
+???+ "📚 Official Documentation"
+    1. [Saving and Loading Models](https://pytorch.org/tutorials/beginner/saving_loading_models.html) - Model persistence guide
+    2. [TorchScript](https://pytorch.org/docs/stable/jit.html) - TorchScript documentation
+    3. [ONNX Export](https://pytorch.org/docs/stable/onnx.html) - ONNX model export
+    4. [Checkpointing](https://pytorch.org/tutorials/recipes/recipes/saving_and_loading_models_for_inference.html) - Checkpoint management
+
+???+ "📖 Essential Articles"
+    1. [Model Serialization](https://pytorch.org/tutorials/beginner/saving_loading_models.html) - Saving models tutorial
+    2. [TorchScript Tutorial](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html) - TorchScript introduction
+    3. [ONNX Tutorial](https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html) - ONNX export and inference
+    4. [Model Versioning](https://pytorch.org/tutorials/recipes/recipes/saving_and_loading_models_for_inference.html) - Best practices
+
+???+ "🎓 Learning Resources"
+    1. [Production Deployment](https://pytorch.org/tutorials/intermediate/flask_rest_api_tutorial.html) - Deploying models
+    2. [Model Optimization](https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html) - Optimizing for production
+    3. [Model Quantization](https://pytorch.org/docs/stable/quantization.html) - Quantization techniques
+
+???+ "💡 Best Practices"
+    1. [Save vs Load Patterns](https://pytorch.org/tutorials/beginner/saving_loading_models.html) - When to save what
+    2. [State Dict vs Full Model](https://pytorch.org/tutorials/beginner/saving_loading_models.html) - Choosing save format
+    3. [Cross-Platform Compatibility](https://pytorch.org/docs/stable/jit.html) - Ensuring compatibility
+
+???+ "🔬 Research Papers"
+    1. [ONNX: Open Neural Network Exchange](https://onnx.ai/) - ONNX format specification
+    2. [Model Compression](https://arxiv.org/abs/1710.09282) - Compression techniques
 
-**Reference:**
-- [Saving and Loading Models](https://pytorch.org/tutorials/beginner/saving_loading_models.html)
-- [TorchScript Documentation](https://pytorch.org/docs/stable/jit.html)
+---

docs/courses/pytorch/16-gpu-acceleration.md

@@ -607,9 +607,32 @@ Continue to [Chapter 17: Mixed Precision & Optimization](17-mixed-precision.md)
 - ✅ Gradient accumulation simulates larger batches
 - ✅ Always clear GPU cache between experiments
 
----
+## Recommended Reads
+
+???+ "📚 Official Documentation"
+    1. [CUDA Semantics](https://pytorch.org/docs/stable/notes/cuda.html) - CUDA operations and memory
+    2. [Distributed Training](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) - DDP tutorial
+    3. [Automatic Mixed Precision](https://pytorch.org/docs/stable/amp.html) - AMP documentation
+    4. [Multi-GPU Training](https://pytorch.org/tutorials/beginner/dist_overview.html) - Multi-GPU strategies
+
+???+ "📖 Essential Articles"
+    1. [GPU Acceleration Tutorial](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html) - GPU basics
+    2. [Distributed Data Parallel](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) - DDP guide
+    3. [Mixed Precision Training](https://pytorch.org/docs/stable/amp.html) - AMP tutorial
+    4. [GPU Memory Management](https://pytorch.org/docs/stable/notes/cuda.html#memory-management) - Memory optimization
+
+???+ "🎓 Learning Resources"
+    1. [Performance Optimization](https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html) - Optimization guide
+    2. [Profiling PyTorch](https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html) - Performance profiling
+    3. [Gradient Accumulation](https://pytorch.org/docs/stable/notes/autograd.html#gradient-accumulation) - Large batch training
+
+???+ "💡 Best Practices"
+    1. [GPU Best Practices](https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html) - Optimization tips
+    2. [Memory Efficient Training](https://pytorch.org/docs/stable/notes/cuda.html#memory-management) - Reducing memory usage
+    3. [Multi-GPU Setup](https://pytorch.org/tutorials/beginner/dist_overview.html) - Setting up multi-GPU
+
+???+ "🔬 Research Papers"
+    1. [Mixed Precision Training](https://arxiv.org/abs/1710.03740) - FP16 training paper
+    2. [Distributed Training](https://arxiv.org/abs/2006.15704) - Large-scale training
 
-**Reference:**
-- [CUDA Semantics](https://pytorch.org/docs/stable/notes/cuda.html)
-- [Distributed Training](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html)
-- [AMP Documentation](https://pytorch.org/docs/stable/amp.html)
+---

docs/courses/pytorch/17-deployment.md

@@ -772,9 +772,32 @@ You've completed the PyTorch deployment guide! Practice by:
 - ✅ Version control your models
 - ✅ Test thoroughly before deploying
 
----
+## Recommended Reads
+
+???+ "📚 Official Documentation"
+    1. [TorchScript](https://pytorch.org/docs/stable/jit.html) - TorchScript documentation
+    2. [ONNX Export](https://pytorch.org/docs/stable/onnx.html) - ONNX model export
+    3. [TorchServe](https://pytorch.org/serve/) - Model serving framework
+    4. [Model Optimization](https://pytorch.org/docs/stable/quantization.html) - Quantization and optimization
+
+???+ "📖 Essential Articles"
+    1. [TorchScript Tutorial](https://pytorch.org/tutorials/beginner/Intro_to_TorchScript_tutorial.html) - TorchScript guide
+    2. [ONNX Tutorial](https://pytorch.org/tutorials/advanced/super_resolution_with_onnxruntime.html) - ONNX export
+    3. [Model Deployment](https://pytorch.org/tutorials/intermediate/flask_rest_api_tutorial.html) - REST API deployment
+    4. [Production Best Practices](https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html) - Production tips
+
+???+ "🎓 Learning Resources"
+    1. [Deployment Tutorials](https://pytorch.org/tutorials/intermediate/flask_rest_api_tutorial.html) - Deployment examples
+    2. [Model Serving](https://pytorch.org/serve/) - Serving models at scale
+    3. [Edge Deployment](https://pytorch.org/mobile/home/) - Mobile and edge deployment
+
+???+ "💡 Best Practices"
+    1. [Model Optimization](https://pytorch.org/docs/stable/quantization.html) - Quantization strategies
+    2. [Performance Monitoring](https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html) - Monitoring deployed models
+    3. [Version Control](https://pytorch.org/tutorials/recipes/recipes/saving_and_loading_models_for_inference.html) - Model versioning
+
+???+ "🔬 Research Papers"
+    1. [ONNX: Open Neural Network Exchange](https://onnx.ai/) - ONNX specification
+    2. [Model Compression](https://arxiv.org/abs/1710.09282) - Compression techniques
 
-**Reference:**
-- [TorchScript Documentation](https://pytorch.org/docs/stable/jit.html)
-- [ONNX Documentation](https://onnx.ai/)
-- [TorchServe](https://pytorch.org/serve/)
+---

docs/courses/pytorch/18-debugging-visualization.md

@@ -649,8 +649,28 @@ Continue to [Chapter 19: Advanced Topics](19-advanced-topics.md) for:
 - ✅ Monitor gradient norms
 - ✅ Profile code to find bottlenecks
 
----
+## Recommended Reads
+
+???+ "📚 Official Documentation"
+    1. [PyTorch Debugging](https://pytorch.org/tutorials/recipes/recipes/debugging_recipe.html) - Debugging guide
+    2. [TensorBoard](https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html) - TensorBoard tutorial
+    3. [Profiler](https://pytorch.org/docs/stable/profiler.html) - Performance profiler
+    4. [Visualization Tools](https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html) - Visualization utilities
+
+???+ "📖 Essential Articles"
+    1. [Debugging Techniques](https://pytorch.org/tutorials/recipes/recipes/debugging_recipe.html) - Common debugging strategies
+    2. [TensorBoard Integration](https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html) - Using TensorBoard
+    3. [Gradient Checking](https://pytorch.org/tutorials/beginner/examples_autograd/two_layer_net_custom_function.html) - Verifying gradients
+    4. [Model Inspection](https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html) - Inspecting models
+
+???+ "🎓 Learning Resources"
+    1. [Visualization Best Practices](https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html) - Visualization tips
+    2. [Performance Profiling](https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html) - Profiling guide
+    3. [Debugging Workflows](https://pytorch.org/tutorials/recipes/recipes/debugging_recipe.html) - Debugging workflows
+
+???+ "💡 Best Practices"
+    1. [Common Debugging Issues](https://pytorch.org/tutorials/recipes/recipes/debugging_recipe.html) - Troubleshooting
+    2. [Monitoring Training](https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html) - Training monitoring
+    3. [Visualization Tools](https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html) - Tool selection
 
-**Reference:**
-- [PyTorch Debugging](https://pytorch.org/tutorials/recipes/recipes/debugging_recipe.html)
-- [TensorBoard Tutorial](https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html)
+---

docs/courses/pytorch/19-advanced-topics.md

@@ -594,9 +594,33 @@ You've completed the advanced topics! Keep learning:
 - ✅ Interpretability tools explain model decisions
 - ✅ Stay updated with latest research
 
----
+## Recommended Reads
+
+???+ "📚 Official Documentation"
+    1. [PyTorch Lightning](https://www.pytorchlightning.ai/) - High-level training framework
+    2. [Weights & Biases](https://wandb.ai/) - Experiment tracking
+    3. [Ray Tune](https://docs.ray.io/en/latest/tune/index.html) - Hyperparameter tuning
+    4. [Hugging Face Transformers](https://huggingface.co/transformers/) - Pre-trained transformers
+
+???+ "📖 Essential Articles"
+    1. [PyTorch Lightning Tutorial](https://pytorch-lightning.readthedocs.io/) - Lightning guide
+    2. [Experiment Tracking](https://wandb.ai/site) - W&B tutorial
+    3. [Hyperparameter Tuning](https://docs.ray.io/en/latest/tune/index.html) - Tuning strategies
+    4. [Advanced Architectures](https://pytorch.org/tutorials/beginner/introyt/introyt1_tutorial.html) - Modern architectures
+
+???+ "🎓 Learning Resources"
+    1. [Vision Transformers](https://pytorch.org/vision/stable/models.html) - ViT tutorial
+    2. [Model Interpretability](https://pytorch.org/tutorials/beginner/introyt/introyt1_tutorial.html) - Explainability
+    3. [Advanced Training Techniques](https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html) - Advanced methods
+
+???+ "💡 Best Practices"
+    1. [Framework Selection](https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html) - Choosing frameworks
+    2. [Experiment Management](https://wandb.ai/site) - Managing experiments
+    3. [Staying Updated](https://pytorch.org/blog/) - Following latest research
+
+???+ "🔬 Research Papers"
+    1. [An Image is Worth 16x16 Words](https://arxiv.org/abs/2010.11929) - Vision Transformer
+    2. [BERT: Pre-training of Deep Bidirectional Transformers](https://arxiv.org/abs/1810.04805) - BERT paper
+    3. [Attention Is All You Need](https://arxiv.org/abs/1706.03762) - Transformer architecture
 
-**Reference:**
-- [PyTorch Lightning](https://www.pytorchlightning.ai/)
-- [Weights & Biases](https://wandb.ai/)
-- [Ray Tune](https://docs.ray.io/en/latest/tune/index.html)
+---

docs/courses/pytorch/20-best-practices.md

@@ -914,8 +914,32 @@ You've completed the PyTorch cheatsheet! Now you can:
 
 **Congratulations!** You now have a comprehensive understanding of PyTorch. Keep building and learning! 🚀
 
-**Helpful Resources:**
-- [PyTorch Documentation](https://pytorch.org/docs/)
-- [PyTorch Tutorials](https://pytorch.org/tutorials/)
-- [PyTorch Forums](https://discuss.pytorch.org/)
-- [Papers with Code](https://paperswithcode.com/)
+## Recommended Reads
+
+???+ "📚 Official Documentation"
+    1. [PyTorch Documentation](https://pytorch.org/docs/) - Complete API reference
+    2. [PyTorch Tutorials](https://pytorch.org/tutorials/) - Official tutorials
+    3. [Best Practices Guide](https://pytorch.org/tutorials/recipes/recipes_index.html) - PyTorch recipes
+    4. [Performance Tuning](https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html) - Optimization guide
+
+???+ "📖 Essential Articles"
+    1. [PyTorch Best Practices](https://pytorch.org/tutorials/recipes/recipes_index.html) - Official best practices
+    2. [Common Mistakes](https://github.com/Kaixhin/grokking-pytorch) - Common pitfalls
+    3. [Production Deployment](https://pytorch.org/tutorials/intermediate/flask_rest_api_tutorial.html) - Deployment guide
+    4. [Code Organization](https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html) - Project structure
+
+???+ "🎓 Learning Resources"
+    1. [PyTorch Forums](https://discuss.pytorch.org/) - Community discussions
+    2. [Papers with Code](https://paperswithcode.com/) - Research implementations
+    3. [PyTorch Blog](https://pytorch.org/blog/) - Latest updates and tutorials
+
+???+ "💡 Best Practices"
+    1. [Reproducibility](https://pytorch.org/docs/stable/notes/randomness.html) - Ensuring reproducibility
+    2. [Code Quality](https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html) - Writing clean code
+    3. [Testing Strategies](https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html) - Testing models
+
+???+ "🔬 Research Papers"
+    1. [PyTorch: An Imperative Style Deep Learning Library](https://papers.nips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library) - PyTorch paper
+    2. [Deep Learning Best Practices](https://arxiv.org/abs/1706.02677) - Training deep networks
+
+---