handshake

# handshake
    Pre-contextual Foundational model for learning heustric behaviors. Within and out of professional 
    environments. 

This foundational deep learning model is designed to serve as a starting point for training various 3D heustric behaviors and capabilities. It will be built upon established deep learning architectures and techniques, providing a robust and flexible foundation for further development and customization.

## disclaimer 
    the context of this document is subjective to blackbox test driven development. So, it's knowledge is limited to lingo exposure.
    furthermore each definition found along this document. Are just objective to purpose. and will be iteratively edited with advancements.
    for solicit feedback feel free to interactively contact me: [email] [email protected] 
## Model Architecture
 The model architecture consists of the following key components. To which I will release later :-

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## Training Objectives
    The foundational deep learning model is designed to be trained on a diverse set of datasets, with the goal of developing a robust versitile transformer model. Some of the key training objectives. Will be include later per the following list :-

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## Training Strategies
    To achieve the desired training objectives, the following strategies will be employed:

1. **Multi-Task Architecture**: The model will be built on driven sets of task according to datasets. Encouraging the development of general-purpose capabilities.
2. **Adversarial Training**: The model will be exposed to adversarial contiguous data for pre-contextual transformations while improving it's robustness and generalization.
3. **Knowledge Distillation**: Techniques like knowledge distillation will be used to transfer knowledge from this model to other models. For productive model transformations
4. **Architectural Exploration**: The model architecture will be iteratively refined and optimized per tasks. Through techniques like neural architecture and hyperparameter tuning

## Applications
    The foundational deep learning model can be applied to a wide range of tasks and domains, including:

- **Natural Language Processing**: Text generation fine tuning, cultural, and translation.
- **Computer Vision**: auto image annotation, object categorical labeling, and mcp text translation.
- **Multimodal Learning**: Combining and reasoning over different modes such as object categories.
- **Reinforcement Learning**: Developing intelligent agents that can learn and make decisions in complex environments.

## Ongoing Development and Contributions
    to be mentioned