PyTorch Learning Repository 🚀

This repository contains my learning journey and hands-on practice with PyTorch, where I explored fundamental concepts of deep learning and implemented multiple models and projects.

📚 Learning Source

I learned PyTorch primarily from the CampusX YouTube channel. Some parts of the code are inspired by the mentor’s implementation, while many concepts and models were practiced and modified by me for better understanding.

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🧠 Topics Covered

🔹 PyTorch Basics

• Tensors and operations
• Autograd (automatic differentiation)
• Computational graphs
• GPU training basics
• Data loading using Dataset and DataLoader

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🔹 Key Deep Learning Concepts

• Forward and backward propagation
• Loss functions and optimizers
• Model training and evaluation loops
• Overfitting and regularization basics

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🔹 Artificial Neural Networks (ANN)

• Fully connected neural networks
• Activation functions (ReLU, Sigmoid, etc.)
• Building custom models using nn.Module

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🔹 Convolutional Neural Networks (CNN)

• Convolution and pooling layers
• Feature extraction from images
• Building CNN architectures for classification tasks

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🔹 Recurrent Neural Networks (RNN)

• Sequential data handling
• Basic RNN implementation
• Limitations of vanilla RNN

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🔹 LSTM (Long Short-Term Memory)

• Understanding long-term dependencies
• Implementing LSTM networks
• Sequence prediction tasks

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💻 Projects

🧥 Image Classification (Fashion MNIST)

• Built a CNN model to classify clothing items

• Dataset: Fashion MNIST

• Tasks performed:

  • Data preprocessing
  • Model training and validation
  • Performance evaluation

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✍️ Next Word Prediction (LSTM)

• Implemented an LSTM-based model
• Trained on text data for sequence prediction
• Predicts the next word given a sequence

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🛠️ Tech Stack

• Python
• PyTorch
• NumPy
• Matplotlib

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📌 Notes

• Some code snippets are adapted from the mentor’s tutorials
• Most implementations were re-written and experimented with for deeper understanding
• This repository is focused on learning and practice, not production-ready code

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🎯 Future Improvements

• Add more advanced architectures (Transformers)
• Improve model performance with tuning
• Deploy models using Flask or Streamlit

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🤝 Acknowledgment

Special thanks to the CampusX YouTube channel for providing clear and structured guidance on PyTorch and deep learning.

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⭐ Conclusion

This repository reflects my foundational understanding of PyTorch and deep learning concepts through practical implementation. It serves as a stepping stone for more advanced AI and deep learning projects.

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