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AlphaTree is an educational repository that provides a visual roadmap of deep learning models and related artificial intelligence technologies. The project focuses on explaining the historical development and relationships between major neural network architectures used in modern machine learning. It presents diagrams and documentation describing the evolution of models such as LeNet, AlexNet, VGG, ResNet, DenseNet, and Inception networks. The repository organizes these architectures into a structured learning path that helps learners understand how deep learning models improved over time through changes in depth, architectural complexity, and training techniques. In addition to neural networks used for image classification, the project also references broader AI fields such as generative adversarial networks, natural language processing, and graph neural networks.
Features
- Visual roadmap illustrating the evolution of deep neural network architectures
- Educational explanations of models such as AlexNet, VGG, and ResNet
- Documentation covering advances in training methods and model design
- Reference links to research papers and implementation examples
- Conceptual overview of related AI fields including GANs and NLP
- Learning guide that organizes deep learning models into a historical progression
Project Samples
