Techniques

Artificial intelligence is anchored by a vast array of techniques that define its functionality and capabilities.

Dive into the stages of AI model progression:

• Data Handling: Grasp the intricacies of sourcing, cleaning, and preprocessing data—the foundation of any AI system.
• Training & Validation: Understand the balance of model learning, the art of avoiding overfitting, and the pursuit of generality.
• Deployment & Scaling: Discover how trained models are seamlessly integrated into real-world applications and scaled to handle vast datasets.

Venture into the blueprint of AI systems:

• Classical Models: Revisit the foundational architectures that paved the way, from decision trees to support vector machines.
• Neural Networks: Dive deep into the layers of perceptrons, convolutional networks, and recurrent models.
• Transformative Designs: Encounter advanced structures like transformers, responsible for breakthroughs such as GPT and BERT.

For both budding AI enthusiasts and seasoned experts, the "Techniques" section serves as a bridge to the underlying processes and methods that make AI the formidable force it is today.