Deep Learning Specialization - Andrew Ng (Coursera)

This repository contains complete solutions, Labs, assignments and implementations for Andrew Ng’s Deep Learning Specialization on Coursera. The specialization is a comprehensive series of five courses designed to provide a solid foundation and advanced skills in deep learning.

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About the Specialization

The Deep Learning Specialization covers essential deep learning topics, including:

• Neural Networks & Deep Learning
  Foundations of neural networks, forward and backward propagation, vectorization techniques.

• Improving Deep Neural Networks
  Hyperparameter tuning, regularization methods, optimization algorithms like Adam and RMSProp.

• Structuring Machine Learning Projects
  Strategies for designing and debugging ML systems, error analysis, and best practices.

• Convolutional Neural Networks (CNNs)
  CNN architectures, object detection, image segmentation, Face Recognition and style transfer.

• Sequence Models
  Recurrent neural networks (RNNs), LSTM, GRU, NLP basics, and Transformers using HuggingFace.

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Repository Contents

• Course-wise folders containing notebooks with detailed explanations and TensorFlow implementations.
• Hands-on assignments that reinforce theoretical concepts with practical coding exercises.
• Notes and summaries highlighting key ideas and best practices for each course.
• Project structure tips to help build scalable and maintainable deep learning projects.

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Technologies Used

• Python 3.x
• TensorFlow 2.x
• NumPy, Pandas, Matplotlib
• HuggingFace Transformers (for sequence models)

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How to Use This Repository

1. Clone the repository:

   git clone https://github.com/a1mohamad/DeepLearning_specialization.git

2. Navigate to the course folder you want to study.

3. Follow along with the notebooks and run the code using Jupyter or any Python IDE.

4. Experiment with labs and the assignments for deeper understanding.

HAPPY LEARNING ! 🚀