🚀 Ali3brt's Machine Learning and Deep Learning Projects 🌟

Welcome to the Ali3brt Repository! 🧠💻 This collection features cutting-edge projects in machine learning, deep learning, and artificial intelligence. From generative models to object detection and trading strategies, this repository has it all! 🎨📈

---

📂 Repository Contents

🎨 Generative Models

• 🖼️ 01_deep_conv_gan.ipynb
  Implementation of a Deep Convolutional GAN (DCGAN) to generate realistic images.
• 🧩 02_Autoencoder.ipynb
  Exploration of basic autoencoders for dimensionality reduction and feature learning.
• 🌟 denoising_autoencoder_with_dropout.ipynb
  A denoising autoencoder with dropout to remove noise from images.
• 🎨 Generative_AI(Autoencoder,_Variational_Autoencoder,_GANs).ipynb
  Comprehensive notebook covering Autoencoders, Variational Autoencoders, and GANs.
• 🔮 variational_autoencoders_(1).ipynb
  Implementation of Variational Autoencoders (VAE) for generative modeling.

---

🤖 GAN Architectures

• 🔄 02_cyclegan.ipynb
  Implementation of CycleGAN for image-to-image translation without paired data.
• 🧑‍🎨 03_pix2pix.ipynb
  Pix2Pix GAN implementation for paired image-to-image translation tasks.
• 💡 04_GANs.ipynb
  Exploration of advanced GAN architectures for generative tasks.

---

🖼️ Vision and Object Detection

• 🖌️ 01_CLIP_VIT.ipynb
  Exploration of OpenAI's CLIP model using Vision Transformers (ViT) for image-text tasks.
• ✂️ 03_FastSAM.ipynb
  Implementation of the FastSAM model for fast and efficient image segmentation tasks.
• 🎯 04_Yolo_Object_Detection_Roboflow.ipynb
  YOLO-based object detection using the Roboflow dataset.

---

🌀 Stable Diffusion

• ✨ 02_stable_diffusion.ipynb
  Implementation and exploration of Stable Diffusion, a state-of-the-art generative model for image synthesis and editing.

---

📚 Text Classification

• 📝 ALI_Task0_Text_Classification.ipynb
  Implementation of a text classification model.
• 📄 ALI_Task0_Text_Classification.docx
  A document summarizing the text classification task.
• 📂 ALI_Task0_Text_Classification.txt
  Supporting data or script for the text classification project.

---

📊 Trading Strategy Backtesting

• 💹 Backtesting_Trading_Strategy_using_RSI_&_MA_indicators_with_a_stop_loss_ipynb_txt.ipynb
  Backtesting trading strategies using RSI and Moving Average indicators, including stop-loss functionality.

---

🔧 Requirements

🛠️ Dependencies: The repository uses popular Python libraries. Make sure the following are installed:

• 🐍 TensorFlow
• 🐍 PyTorch
• 🐍 NumPy
• 🐍 Matplotlib
• 🐍 Scikit-learn
• 🐍 OpenCV
• 🤗 HuggingFace Transformers (for Stable Diffusion)
• 🦾 Roboflow (for object detection tasks)

To install any missing libraries:

pip install <library-name>

---

🚀 How to Use

1. Clone the repository:

   git clone https://github.com/ali3brt/your-repository-name.git

2. Open the desired notebook in Google Colab or Jupyter Notebook.
3. Install dependencies and run the cells sequentially. 🎯

---

📄 License

This repository is licensed under the MIT License. Feel free to use, modify, and share! 🤝

---

🤝 Contributions

💡 Have an idea? Want to improve something?
Feel free to fork the repository, make changes, and submit a pull request! Contributions are always welcome! 💪✨

---

📬 Contact

📧 Email: [email protected]
💼 LinkedIn: Ali3brt's LinkedIn

---

✨ Explore, Learn, and Innovate!

We hope you find these projects helpful and inspiring! Let us know what you build with them! 🌟

---