🛡️ Generate synthetic data for intrusion detection systems using GANs to improve performance on NSL-KDD and UNSW-NB15 datasets.

🧵 Classify fashion images using a compact CNN model in TensorFlow/Keras, designed for the Fashion-MNIST dataset with easy execution in Google Colab.

🍇 Classify grape leaf diseases with a CNN to enhance vineyard health, reduce losses, and enable early detection for better grape quality.

🎥 Generate long videos with advanced Mixture-of-Groups Attention, improving the quality and coherence of end-to-end video production.

🧠 Detect neurodegenerative patterns early using advanced machine learning with ADNI data for improved insights and outcomes.

🔍 Classify 1D signal data using a CNN + Transformer model, enabling advanced sequence modeling for biological and physical signals.

🧠 Detect neurodegenerative patterns early using advanced machine learning models with ADNI data for impactful health insights.

🧠 Compare CNN and MLP models for handwritten digit recognition on the MNIST dataset, enhancing your skills in image classification and model optimization.

🧠 Implement convolutional and recurrent neural networks in TensorFlow Keras to handle image and text datasets, enhancing model performance through advanced techniques.

🎯 Develop a deep learning model to accurately predict breast cancer using PyTorch, achieving approximately 97% accuracy for improved early detection.

🖋️ Convert images of mathematical content to LaTeX code with ease using LaTeX-OCR, simplifying the workflow for students and researchers alike.

🤖 Build a Transformer-based gesture recognition system with PyTorch, ONNX, and Gradio for real-time video analysis and efficient inference.

🔗 Simplify your cryptocurrency tasks with pycoin, a Python library providing essential utilities for Bitcoin and alt-coins, ensuring seamless transactions and operations.

📀 Run a fun DVD bouncing screen saver in your terminal with easy setup, perfect for adding a playful touch during idle time.

🌍 Classify agricultural and non-agricultural land in gigapixel-scale satellite imagery with memory-efficient CNN and ViT models for real-world applications.

🔍 Enhance medical imaging with a lightweight CNN model that offers over 91% accuracy and integrated explainability for better clinical trust.

🔍 Detect phishing websites offline using a combined CNN and LSTM model, analyzing URL features for high accuracy in classification.

🧠 Build and understand core deep learning architectures from scratch using NumPy, exploring the fundamentals behind AI through hands-on coding.

🔍 Monitor network activity in real-time with LazyNet, a terminal-based tool that provides clear insights and management for processes and connections.

🧠 Recognize facial emotions using deep learning with Keras and TensorFlow, featuring CNN training, data preprocessing, and visualization tools.