Deep Learning Specialization Coursera

This repository contains all assignments and labs from the Deep Learning Specialization offered by Coursera and deeplearning.ai. The specialization is designed to provide a comprehensive introduction to deep learning, covering a wide range of topics and practical implementations.

Table of Contents

• Courses
  • Course 1: Neural Networks and Deep Learning
  • Course 2: Improving Deep Neural Networks
  • Course 3: Structuring Machine Learning Projects
  • Course 4: Convolutional Neural Networks
  • Course 5: Sequence Models
• Getting Started
• Contributing
• License

Courses

Course 1: Neural Networks and Deep Learning

• Introduction to deep learning
• Basics of neural networks
• Forward and backward propagation
• Optimization algorithms

Course 2: Improving Deep Neural Networks

• Hyperparameter tuning
• Regularization
• Batch normalization
• Optimization techniques

Course 3: Structuring Machine Learning Projects

• Machine learning strategy
• Evaluation metrics
• Error analysis
• Orthogonalization

Course 4: Convolutional Neural Networks

• Convolution operation
• Deep convolutional models
• Object detection
• Neural style transfer

Course 5: Sequence Models

• Recurrent neural networks (RNNs)
• LSTMs and GRUs
• Sequence-to-sequence models
• Attention mechanisms

Getting Started

To get started with the assignments and labs, follow these steps:

1. Clone this repository:

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

2. Navigate to the specific course directory:

   cd deep-learning-specialization/Course-1

3. Follow the instructions in the individual assignment and lab notebooks to complete the exercises.

Contributing

Contributions are welcome! If you have any suggestions, improvements, or bug fixes, please create an issue or submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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