Welcome to my Deep Learning repository!
This is a collection of notebooks, experiments, and mini-projects I created while learning and practicing Deep Learning concepts — from the fundamentals to more advanced topics like CNNs, RNNs,Transformers, and Transfer Learning.
This repository documents my learning path in Deep Learning.
It includes experiments with different architectures, frameworks, and datasets — all focused on understanding how neural networks learn, generalize, and perform on real-world data.
Deep-learning/
│
├── 📂 models/ # Custom-built model architectures
├── 📂 notebooks/ # Jupyter notebooks covering deep learning topics
├── 📂 utils/ # Helper functions (data loaders, visualization, metrics)
└── README.md
- Python 3
- TensorFlow / Keras
- PyTorch
- NumPy, Pandas, Matplotlib, Seaborn
- Jupyter Notebook
Through this repository, my main objectives were to:
- Master PyTorch by building and training models from scratch
- Understand the core theory and intuition behind neural networks
- Gain hands-on experience with different architectures (CNNs, RNNs, Transformers)
- Learn how to debug, tune, and visualize training progress
- Apply transfer learning and model optimization in practice
- 🖼️ Image Classification using CNNs (MNIST, CIFAR-10)
- 🕒 Sequence Prediction using RNN/LSTM
- 🔍 Transfer Learning with pre-trained models (VGG16, ResNet)
- 📊 Visualization of Training Metrics
During my learning, I followed materials from:
- Deep Learning Specialization by Andrew Ng (Coursera)
- A deep understanding of deep learning by udemy
- DataCamp courses.
- Deep Learning Foundations and Concepts book by Christopher M. Bishop.
This repository reflects my personal learning process —
but if you spot improvements, suggestions, or better approaches, feel free to open an issue or pull request!
If you find this repository helpful or inspiring,
please ⭐️ star this repo — it helps others discover it too!
Made with ❤️ and curiosity by Mohamed Diaa Zellagui
"The best way to learn is by building and experimenting."