Ntropy Network

The funding round was led by Finality Capital and Paper Ventures, with significant participation from Draper Dragon, Arrington Capital, Lightshift, Zero Knowled

Holonym Foundation Raises $5.5 Million To Provide Global Digital Personhood With Human Keys

Entropy Network is  now officially live for the general public. This groundbreaking network is designed to integrate businesses into a blockchain network.

Introducing Entropy Mainnet: A Gateway to Experience and Earnings (X&E)

AI-driven PINNs simulate shock waves, collisions, and entropy solutions—advancing fluid dynamics beyond traditional models.

Shocks, Collisions, and Entropy—Neural Networks Handle It All

Particularly over the last several years, researchers across a spectrum of scientific disciplines have studied the dynamics of social media networks to understand how information propagates as the networks evolve. Social media platforms like Twitter and Facebook include not only actual human users but also bots, or automated programs, that can significantly alter how certain messages are spread. While some information-gathering bots are beneficial or at least benign, it was made clear by the 2016 U.S. Presidential election and the 2017 elections in France that bots and sock puppet accounts (that is, numerous social accounts controlled by a single person) were effective in influencing political messaging and <a href="https://medium.com/data-for-democracy/sockpuppets-secessionists-and-breitbart-7171b1134cd5" target="_blank">propagating misinformation on Twitter and Facebook</a>. It is thus crucial to identify and classify social bots to combat the <a href="https://www.washingtonpost.com/news/the-switch/wp/2017/10/05/russian-propaganda-may-have-been-shared-hundreds-of-millions-of-times-new-research-says/" target="_blank">spread of misinformation</a> and especially the propaganda of enemy states and violent extremist groups. This article is a brief summary of my recent bot detection research. It describes the techniques I applied and the results of identifying battling groups of viral bots and cyborgs that seek to sway opinions online.

How Bots and Cyborgs Spread Misinformation: A Data Scientist Finds 5,000+ Bots in 72,000,000+…

An in-depth analysis of cross-entropy loss in Transformer networks, including its connection to attention, theoretical bounds, and empirical observations.

Cross-Entropy Loss Analysis in Transformer Networks

A Non-Diffusive Neural Network Method for Solving Hyperbolic Conservation Laws

Explore why quadratic cost functions hinder neural network training and how cross-entropy improves learning efficiency in deep learning models. 

Why Quadratic Cost Functions Are Ineffective in Neural Network Training

A concise list of key academic works informing our research on Transformer model dynamics, cross-entropy loss, and theoretical connections to Hopfield networks.

Transformer Theory & LLM References: Here's What You Should Check Out

Explore the original GPT-2 model's architecture, including its training on WebText, BPE tokenizer, hidden dimensions, and layer parameters

GPT-2 Architecture and Training Details: Parameters & Cross-Entropy Loss

Explore rigorous mathematical proofs, including properties of incomplete gamma functions, Stirling's approximation, and derivations of loss functions

Theoretical Derivations: Cross-Entropy Loss and Energy Functions in LLMs

This work contextualizes large language model dynamics using a review of Hopfield network models  and empirical data on Transformer cross-entropy loss.

Transformer Performance: Hopfield Theory & Cross-Entropy Loss Data

Explore how majorization minimization (MM) technique is used to adapt Hopfield network models to the multi-layered structure of Transformers

Modeling Transformer Layers: Majorization Minimization & Hopfield Networks

NFT networks' resemblance to social networks, explores key properties, and demonstrates the LPPL model's bubble prediction capabilities.

NFT Networks Mirror Social Structures with Influencer Nodes, Community Hubs, & Zipfian Distributions

Learn to build a model for predicting new links in a social graph, demonstrating the use of Graph Neural Networks and DGL for link prediction.

Predicting Links in Graphs with Graph Neural Networks and DGL.ai

This study considers a bivariate jump-diffusion model to describe Bitcoin price dynamics and the number of Google searches affecting the price.

Neural Network for valuing Bitcoin: References 

Multi-class classification NN is used with the softmax activation function on its final layer together with CE loss. The final layer size = classes_number.

Multi-Class Classification: Understanding Activation and Loss Functions in Neural Networks

This rudimentary explanation will give you a framework to understand how all blockchains are technologically structured…

Understanding the 5 Layers of Technology in Blockchain Crypto Networks 

Today, with open source machine learning software libraries such as TensorFlow, Keras or PyTorch we can create neural network, even with a high structural complexity, with just a few lines of code. Having said that, the Math behind neural networks is still a mystery to some of us and having the Math knowledge behind neural networks and deep learning can help us understand what’s happening inside a neural network. It is also helpful in architecture selection, fine-tuning of Deep Learning models, hyperparameters tuning and optimization.

Introduction To Maths Behind Neural Networks

Understanding Convolution Neural Networks

Building a Feedforward Neural Network from Scratch in Python

Feedforward neural networks are also known as <strong>Multi-layered Network of Neurons</strong> (MLN). These networks of models are called feedforward because the information only travels forward in the neural network, through the input nodes then through the hidden layers (single or many layers) and finally through the output nodes. In MLN there are no feedback connections such that the output of the network is fed back into itself. These networks are represented by a combination of many simpler models(sigmoid neurons).

Deep Learning: Feedforward Neural Networks Explained

<span>W</span>riting your first Neural Network can be done with merely a couple lines of code! In this post, we will be exploring how to use a package called Keras to build our first neural network to predict if house prices are above or below median value. In particular, we will go through the full Deep Learning pipeline, from:

Build your first Neural Network to predict house prices with Keras

After having completed the <a href="http://deeplearning.ai/">deeplearning.ai</a> Deep Learning specialization taught by Andrew Ng, I have decided to work through some of the assignments of the specialization and try to figure out the code myself without only filling in certain parts of it. Doing so, I want to deepen my understanding of neural networks and help others gain intuition by documenting my progress in articles. The complete notebook is available <a href="https://github.com/lksfr/TowardsDataScience/blob/master/NN_with_numpy.ipynb">here</a>.

Building a Neural Network Only Using NumPy

The following contains notes and <a href="https://hackernoon.com/tagged/python" target="_blank">python</a> code, compiled from a lecture given by <a href="http://www.fast.ai/about/#jeremy" target="_blank">Jeremy Howard</a>, co-founder of <a href="http://course.fast.ai" target="_blank">fast.ai</a>. Many thanks to Jeremy and <a href="http://www.fast.ai/about/#rachel" target="_blank">Rachel Thomas</a> for building fast.ai and the <a href="https://github.com/fastai/fastai/" target="_blank">fast.ai library</a>, a high-level wrapper for <a href="http://pytorch.org" target="_blank">PyTorch</a>. The following code is based on the fast.ai libary. For more information, watch the first lesson (of seven) in Practical <a href="https://hackernoon.com/tagged/deep-learning" target="_blank">Deep Learning</a> For Coders, Part 1, which is publically available free of charge. If you are keen to learning deep learning, you won’t regret it!

Image Classification with Convolutional Neural Networks

Ntropy is building a data layer to combine datasets across organizations

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Ntropy Network's stories on HackerNoon

Holonym Foundation Raises $5.5 Million To Provide Global Digital Personhood With Human Keys

Introducing Entropy Mainnet: A Gateway to Experience and Earnings (X&E)

Shocks, Collisions, and Entropy—Neural Networks Handle It All

How Bots and Cyborgs Spread Misinformation: A Data Scientist Finds 5,000+ Bots in 72,000,000+…

Cross-Entropy Loss Analysis in Transformer Networks

A Non-Diffusive Neural Network Method for Solving Hyperbolic Conservation Laws

Why Quadratic Cost Functions Are Ineffective in Neural Network Training

Transformer Theory & LLM References: Here's What You Should Check Out

GPT-2 Architecture and Training Details: Parameters & Cross-Entropy Loss

Theoretical Derivations: Cross-Entropy Loss and Energy Functions in LLMs

Transformer Performance: Hopfield Theory & Cross-Entropy Loss Data

Modeling Transformer Layers: Majorization Minimization & Hopfield Networks

NFT Networks Mirror Social Structures with Influencer Nodes, Community Hubs, & Zipfian Distributions

Predicting Links in Graphs with Graph Neural Networks and DGL.ai

Neural Network for valuing Bitcoin: References

Multi-Class Classification: Understanding Activation and Loss Functions in Neural Networks

Understanding the 5 Layers of Technology in Blockchain Crypto Networks

Introduction To Maths Behind Neural Networks

Understanding Convolution Neural Networks

Building a Feedforward Neural Network from Scratch in Python

Deep Learning: Feedforward Neural Networks Explained

Build your first Neural Network to predict house prices with Keras

Building a Neural Network Only Using NumPy

Image Classification with Convolutional Neural Networks

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