About GNY Coin
GNY Coin One benefit of blockchain’s decentralized technology is its application beyond cryptocurrencies. They are applying the techology to machine learning – to enable a smarter “thinking” blockchain process. Most machine learning models that have been used have an underlying assumption that some global knowledge is required by its algorithms to function (see fig 1). These are centralized distributed, where the entire data set is loaded into a cloud of distributed nodes so that the two main functions of machine learning are performed in one large library of functions ETL/Exploration and Model Training/Parameter Tuning. Spark, Watson, Azure all use this platform based approach and we are also biased towards methods that have been known to work.
GNY Coin Distributed Deep Learning breaks this pattern of one large platform library by creating two of the smallest configurable self-learning unsupervised neural net nodes – ETL node and ML node – and distributing these nodes into each block of the block chain to have them teach themselves the solution to each problem. The one conceptual problem though of machine learning is that error detection requires global knowledge that gets backpropagated to its constituents. This requirement though is fixed in gny.io’s Distributed Deep Learning systems. Therefore we prefer the term ‘localized models’ and not ‘model free models’, even though both mean a measure of lack of intelligence of constituent parts. In general though, this is similar to the concept of ‘parsimony’, in that we seek the simplest of mechanisms that give rise to emergent properties of prediction. Gny.io is not a crushed up small machine learning library; it is one node of a huge distributed brain.
GNY Coin has configurable ETL microservices and configurable machine learning microservices that can read the entire chain of data or it can read the current block data. Gny.io’s microservices uses deep learning which is a class of machine learning algorithms in the form of a neural network that uses a cascade of layers (tiers) of processing units to extract features from data and make predictive guesses about new data. The smallest ML node system varies the weights and biases to see if a better outcome is obtained using a neural network (see Fig 2).
GNY (GNY ) Storage Key Points
|Circulating Supply||192,376,657.00 GNY|
|Source Code||Click Here To View Source Code|
|Explorers||Click Here To View Explorers|
|Chat||Click Here To Visit|
|Whitepaper||Click Here To View|
|Official Project Website||Click Here To Visit Project Website|
The GNY Platform Provides Its Users With:
GNY Coin ability to harness the power of a blockchain technology that is secure & scalable A launchpad for your own cryptographic tokens, decentralised applications & sidechain projects Access to our commercial grade machine learning directly on chain to realise the true potential of your data GNY Dataplace- a better way to share, invest in, and monetize high quality data and ML.
GNY Does The Heavy Lifting
While launching your tokens, running contracts, or getting set up on other networks requires a high level of skill, we have taken a lot of the hard work out for you. Thus enabling you to make your blockchain wishes come true with essentially just a couple of clicks. The ease of getting started with GNY does not rule out building custom and complicated customizations. From custom machine learning contracts, to developing complex UX front ends to your wallets, the GNY system can scale to the specs your project requires GNY’s customizations and built in functions are powered by some of the highest transaction speeds and lowest costs of any blockchain platform ensuring that your great idea can scale as needed.
Building Blockchain Dapps With Ease
GNY Coin goal of the GNY developer solutions platform is to make launching your blockchain powered project as easy as possible.
That means providing you with easy steps that to allow you to :
Launch your own tokens
Run plug-n-play machine learning contracts.
Build custom ML contracts with our Jupyter Notebooks plug in.
Skin your projects front end with HTML.
Connect to other blockchain systems with cross chain technology.
Collaborate with other individuals, groups or teams with our platform that provides options for private or public data sharing.
Neural Node in a Neural Network
GNY Coin At the heart of Gny.io is the special backpropagation algorithm. Backpropagation of errors and gradient descent are some of optimization methods used to calculate the error contribution of each blockchain node after a batch of data is processed. Gny.io uses a variation of structured streaming AI and parquet data format – this sets the weights in the neuron. This means that the neurons change their connection and their weights to lesson the error. Gny.io will determine the normal rules of the system by itself with unsupervised learning.
Adjusted weights is what is configurable. Backpropagation is an expression for the partial derivative ∂C/∂w of the cost function C with respect to any weight w (or bias b) in the network. The expression tells us how quickly the cost changes when we change the weights and biases. And so backpropagation isn’t just a fast algorithm for learning. It actually gives us detailed insights into how changing the weights and biases, changes the overall behavior of the network. Let W(l)jk be the weight we are trying to find to kth neuron in the (l−1)th layer to the jth neuron in the lth layer. So, for example, the diagram below shows the weight on a connection from the fourth neuron in the second layer to the second neuron in the third layer of a network.
Parquet Data flow
GNY Coin gradient descent optimization is Deep Learning. Deep learning is an advanced form of Artificial Intelligence and a powerful set of techniques for learning in neural networks. Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing, Gny.io is a fusion of Deep Learning technology on to a blockchain.
The blockchain, being a distributed transparent consensus based peer-to-peer network that has been shown to be extremely resilient to adversarial attacks. The blockchain itself creates the neural net. Gny.io is the first to apply this technique in blockchain. Gny.io uses a Probabilistic Graph Model (PGM) approach to DL. In the PGM approach, the neural nodes construct a probabilistic graph that defines the relationship’s different variables. The approach uses Monte-Carlo sampling to construct Bayesian consistent distributions for the variables. We then use Deep Learning to learn from this synthesized data.