/Deep-Learning-101

Deep Learning 101: A beginner's guide to the fundamentals of deep learning. Explore neural networks, CNNs, RNNs, frameworks like TensorFlow and PyTorch, and practical applications. Get hands-on experience and unleash the power of deep learning. No prior experience required. Start your journey now!

Primary LanguageJupyter Notebook

Deep Learning 101

Welcome to Deep Learning 101! This repository serves as a beginner's guide to the fundamentals of deep learning. Whether you are new to the field or looking to expand your knowledge, this repository provides comprehensive resources, including Jupyter notebooks, code examples, and practical exercises.

Table of Contents

  • Introduction to Neural Networks
  • Feedforward Neural Networks
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Deep Learning Frameworks (TensorFlow, PyTorch)
  • Practical Applications

Getting Started

  1. Clone the repository: git clone https://github.com/azimAVI/deep-learning-101.git
  2. Explore the notebooks and code examples in each section.
  3. Follow the step-by-step tutorials and complete the practical exercises.
  4. Experiment with your own data and models.

Requirements

  • Python 3.7 or above
  • Jupyter Notebook
  • TensorFlow 2.x or PyTorch
  • Additional dependencies (specified in requirements.txt)

Contributing

Contributions are welcome! If you find any issues or have suggestions for improvement, please open an issue or submit a pull request. Let's collaborate and make Deep Learning 101 a valuable resource for all learners.

Acknowledgments

I would like to thank the open-source community for their valuable contributions and the authors of the resources referenced in this repository.

Contact

For any questions or inquiries, please reach out to me

Happy learning and happy deep diving into the world of deep learning!