Deep Learning Repository

Overview

Welcome to the Deep Learning Repository! This repository serves as a comprehensive collection of Jupyter notebooks, exploring various deep learning concepts and applications. Whether you are a beginner or an experienced deep learning enthusiast, this repository offers a wide range of practical exercises, models, and predictions to enhance your understanding and expertise.

Project Structure

The repository contains the following Jupyter notebooks:

1. ANN-Customer_Churn_Prediction.ipynb: This notebook focuses on using Artificial Neural Networks (ANN) for customer churn prediction, demonstrating the application of deep learning in a real-world scenario.

2. Gradient_Descent_&_Neural_network_Implementation.ipynb: In this notebook, you will dive into the fundamentals of Gradient Descent and implement a basic neural network from scratch.

3. Practical_workouts.ipynb: it contains Matrices,Activation function and Cost function.

4. RNN-Next_Word_Prediction.ipynb: This notebook delves into Recurrent Neural Networks (RNN) and demonstrates the exciting application of next-word prediction using sequential data.

5. Tensorflow.ipynb: Discover the powerful capabilities of TensorFlow, a popular deep learning framework, in this notebook.

6. Week_Notes: This directory contains detailed notes and summaries from weekly deep learning sessions, offering valuable insights and key takeaways.

Installation

To access and interact with the notebooks in this repository, follow these simple steps:

1. Clone this repository to your local machine:

git clone https://github.com/your-username/deep-learning-repo.git
cd deep-learning-repo

2. Install the required Python libraries:

pip install tensorflow keras pandas numpy matplotlib

3. Launch Jupyter notebook:

jupyter notebook

4. Navigate to the notebook of your interest and start exploring the world of deep learning!

Highlights

• Real-World Applications: Gain practical experience by exploring customer churn prediction and next-word prediction using ANN and RNN, respectively.

• Hands-On Implementation: Dive into the fundamentals and build a basic neural network using Gradient Descent from scratch.

• Weekly Deep Learning Sessions: Access comprehensive notes and summaries from weekly deep learning sessions to expand your knowledge base.

License

This repository is licensed under the MIT License.

Contribute

You are welcome to contribute to the repository by submitting your own deep learning models, exercises, or relevant resources. Feel free to open issues or pull requests to enhance the collection and foster a collaborative learning environment.

Conclusion

Embark on your deep learning journey with this diverse repository, exploring a wide range of topics, applications, and practical exercises. Improve your understanding of deep learning concepts and unleash the power of neural networks in various domains.

Happy learning and happy deep learning adventures!