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https://github.com/saadsalmanakram/rnn-quest
Understanding the architecture behind Recurrent Neural Networks and its types
https://github.com/saadsalmanakram/rnn-quest
deep-learning deep-neural-networks recurrent-networks recurrent-neural-network recurrent-neural-networks
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Understanding the architecture behind Recurrent Neural Networks and its types
- Host: GitHub
- URL: https://github.com/saadsalmanakram/rnn-quest
- Owner: saadsalmanakram
- Created: 2024-08-06T12:32:05.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-01-28T20:33:09.000Z (17 days ago)
- Last Synced: 2025-01-28T21:32:58.018Z (17 days ago)
- Topics: deep-learning, deep-neural-networks, recurrent-networks, recurrent-neural-network, recurrent-neural-networks
- Homepage:
- Size: 102 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
---
# 🚀 RNN-Quest: Mastering Recurrent Neural Networks (RNNs)
Welcome to **RNN-Quest**, your one-stop resource to learn everything about **Recurrent Neural Networks (RNNs)**! Whether you're new to RNNs or looking to dive into advanced architectures, this repository will guide you through the key concepts, implementations, and applications of RNNs.
## 🚀 What You'll Learn
- **📚 RNN Basics:** Architecture, Training, and Backpropagation Through Time (BPTT).
- **🛠️ Implementing RNNs:** Practical code examples with PyTorch and TensorFlow.
- **🔍 Advanced RNN Architectures:** LSTM, GRU, BiRNN, and more.
- **💡 Applications of RNNs:** Time Series Prediction, Text Generation, Language Modeling.
- **⚡ RNN Optimization:** Vanishing/Exploding Gradients, Attention Mechanisms.
## 📌 Topics Covered
### 1️⃣ Introduction to RNNs
- **What is an RNN?** Understanding the basic building blocks of RNNs.
- **Training RNNs** with Backpropagation Through Time (BPTT).
- **RNN vs. Traditional Neural Networks:** Why use RNNs for sequential data?
### 2️⃣ Deep Dive into RNN Architectures
- **Vanilla RNN**: Basic Recurrent Layer and its limitations.
- **Long Short-Term Memory (LSTM)**: Solving the vanishing gradient problem.
- **Gated Recurrent Unit (GRU)**: A simpler alternative to LSTM.
- **Bidirectional RNNs (BiRNNs)**: Capturing both past and future context.
- **Stacked RNNs**: Deep RNNs with multiple layers for complex sequences.
### 3️⃣ Advanced RNN Topics
- **Attention Mechanisms:** Leveraging attention to improve performance on long sequences.
- **Transformer Models**: Moving beyond RNNs with attention-based architectures.
- **Sequence-to-Sequence Models** for tasks like machine translation.
### 4️⃣ RNN Applications
- **Time Series Forecasting:** Predicting stock prices, sales, weather, etc.
- **Natural Language Processing (NLP)**: Language modeling, text generation, and sentiment analysis.
- **Speech Recognition**: Converting speech to text using RNNs.
- **Video Classification**: Using RNNs for video data to classify actions and sequences.
### 5️⃣ Optimizing RNNs
- **Vanishing and Exploding Gradients**: Techniques like gradient clipping and initialization methods.
- **Regularization in RNNs**: Dropout, L2 Regularization, and Layer Normalization.
- **Hyperparameter Tuning for RNNs**: Choosing the best architecture for your task.
### 6️⃣ Reinforcement Learning with RNNs
- **RNNs in RL**: Using RNNs to model environments with partial observability.
- **Memory Networks** for decision-making tasks.
## 🛠️ Tech Stack
- **Python (PyTorch, TensorFlow, Keras)**
- **NumPy & Pandas** for data manipulation.
- **Matplotlib & Seaborn** for visualizations.
- **CUDA (for GPU Acceleration)**
## 📌 Getting Started
1. **Clone the repository**
```bash
git clone https://github.com/saadsalmanakram/RNN-Quest.git
cd RNN-Quest
```
2. **Install dependencies**
```bash
pip install -r requirements.txt
```
3. **Run Example Code**
```bash
python train_rnn.py # Example of training a basic RNN model
python generate_text.py # Generate text with an RNN model
```
4. **Explore Advanced RNNs**
Check out the `advanced/` folder for LSTM, GRU, and BiRNN implementations.
## 📚 Resources & References
- [Understanding LSTMs](https://colah.github.io/posts/2015-08-Understanding-LSTMs/)
- [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)
- [Deep Learning Book by Ian Goodfellow](https://www.deeplearningbook.org/)
## 🤝 Contributing
We welcome contributions! If you have improvements or new RNN-based architectures, feel free to submit a pull request.
## ⭐ Support the Project
If you find this repository helpful, give it a ⭐ and share it with your network!
---