"Deep-chain Learning"

"Deep-chain Learning" unifies deep learning, IoTs and Blockchain technologies in decentralized autonomous organizations (DAOs). Such a model framework enhances computing with learning power for application of existing oversight regimes to financial applications, IOT based applications, smart contracts or self-executing transactions, interactions between humans and machines or between multiple entities automatically enforced by the underlying code. This learning technique seamlessly brings in deep learning in evolving decentralized autonomous organizations that offer new forms of participatory governance and activities. In a unified framework, the layers of abstractions in neural networks used for Deep Learning can be represented by layers of  blocks of variable sizes in chains (or dependencies) over levels of abstractions collectively validating overall learning mechanism. In addition to statistical computing power in neural networks, data in blocks within a layered representation can also be analyzed by calculus based techniques improving overall performance. We use a Mathematical model (CALSTATDN) iterating over a sequence of  computing stages based on Calculus (CAL), Statistics (STAT) and database normalization (DN) in Deep-chain Learning.