🧬 Neural Networks Lab — the neural core of applied AI

[yurcash]

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This section is dedicated to the exploration and practical use of neural networks.
It’s designed for developers, researchers, and AI enthusiasts working with both foundational and cutting-edge architectures — from dense networks and convolutional layers to modern transformers.​

What you’ll find here:​

• Discussions of key frameworks: TensorFlow, PyTorch, Keras, ONNX, and more
• Architectural deep dives: CNN, RNN, LSTM, GANs, Transformers, BERT, GPT, etc.
• Fine-tuning, hyperparameter optimization, and model evaluation
• Data preprocessing, augmentation, and training strategies
• Supervised, unsupervised, and transfer learning approaches
• Real-world applications across domains: computer vision, NLP, bioinformatics, robotics
• Topics on model interpretability, robustness, and reproducibility

Whether you’re experimenting with architectures or applying neural models to production tasks — this is a space for collaboration, insight, and progress.