Welcome to the Deep Learning Essentials, a comprehensive collection of notebooks and algorithms that explore various aspects of deep learning. This repository is designed to serve as both a learning resource and a practical tool for deep learning enthusiasts, professionals, and students alike.
The notes and notebooks included here are inspired by lectures from @IIT Madras, @IIT Bombay, and @Statquest, making them a valuable resource for understanding key deep learning concepts taught by world-renowned faculty. 🎓
The repository is organized into three main folders, each focusing on different aspects of deep learning:
This folder contains hands-on examples and tutorials related to PyTorch, a popular deep learning framework. You'll find examples covering:
- Neural network architectures 🧠
- Training pipelines 🏋️♂️
- Custom datasets 📊
- Model evaluation ✅
- And much more!
These notebooks will help you understand PyTorch's dynamic computational graph and how to use it effectively in deep learning research and projects.
In this folder, you'll find notebooks focusing on TensorFlow, another widely-used framework in the deep learning community. The notebooks cover:
- Building deep neural networks 🏗️
- Implementing transfer learning 🔄
- Distributed training 📡
- Model serving and deployment with TensorFlow 🚀
TensorFlow is highly versatile and used in production environments, making these notebooks invaluable for both learning and applying deep learning in real-world applications.
This folder includes implementations of fundamental optimization algorithms and neural network components built from scratch. Some of the algorithms covered include:
- Gradient Descent (and its variants) 🏃♂️
- Backpropagation 🔄
- Optimization techniques like Adam, RMSProp, etc. ⚙️
By understanding these algorithms at a fundamental level, you'll gain deeper insights into how deep learning models are trained and optimized.
LICENSE: The license file for this repository, outlining terms of use.requirements.txt: A list of dependencies required to run the notebooks and code in this repository..gitignore: Specifies files and directories that should not be tracked by Git.README.md: This file, which provides an overview of the repository and its contents.
- Clone the repository:
git clone https://github.com/yourusername/deep-learning-repo.git
- Install the dependencies:
pip install -r requirements.txt
- Explore the notebooks:
- PyTorch: Navigate to
pytorch/and explore the PyTorch notebooks. - TensorFlow: Navigate to
tensorflow/for TensorFlow-based tutorials. - Algorithms: Dive deep into algorithms from scratch in
optimization/.
- PyTorch: Navigate to
Contributions are welcome! If you'd like to add new notebooks, algorithms, or improve existing content, feel free to submit a pull request. Please make sure your contributions align with the overall structure and standards of the repository.
This project is licensed under the MIT License - see the LICENSE file for details.
🎉 Happy learning and coding! Feel free to reach out if you have any questions or suggestions. Let's build great things with deep learning together! 💡