Machine Learning Techniques

“Machine Learning Techniques” explores various aspects of machine learning, including data augmentation, Python programming, AI applications, and knowledge base infrastructure. The document delves into the importance of understanding and utilizing different techniques to enhance machine learning models effectively. It discusses the challenges faced in the field, such as the need for dynamic data processing and the integration of generative AI into enterprise systems. By leveraging Python programming, AI applications like langchain, and concepts like retrieval-augmented generation, the document provides insights into how to optimize machine learning processes for improved outcomes.

Visit Machine Learning Techniques

A New Type of Non-Standard High Performance DNN with Remarkable Stability

I explore deep neural networks (DNNs) starting from the foundations, introducing a new type of architecture, as much different from machine learning than it is from traditional AI. The original adapti...

New Book: 0 and 1 – From Elemental Math to Quantum AI

Machine Learning Techniques

The book is available on our E-store, here. It all started with the number 1. This e-book offers a trip deep into the most elusive and fascinating multi-century old conjecture in number theory: are th...

Quantum Dynamics, Logistic Map, and Digit Distribution of Special Math Constants

Machine Learning Techniques

Using the logistic map instead of the base quadratic system as in paper 53 (here), I obtain very similar quantum dynamics, this time for the function sin2(√x) instead of exp(x). When x is a small inte...

Doing Better with Less: LLM 2.0 for Enterprise

Machine Learning Techniques

Standard LLMs are trained to predict the next tokens or missing tokens. It requires deep neural networks (DNN) with billions or even trillions of tokens, as highlighted by Jensen Huang, CEO of Nvidia,...

What is LLM 2.0?

Machine Learning Techniques

LLM 2.0 refers to a new generation of large language models that mark a significant departure from the traditional deep neural network (DNN)-based architectures, such as those used in GPT, Llama, Clau...

LLMs – Key Concepts Explained in Simple English, with Focus on LLM 2.0

Machine Learning Techniques

The following glossary features the main concepts attached to LLM 2.0, with examples, rules of thumb, caveats, best practices, contrasted against standard LLMs. For instance, OpenAI has billions of pa...

10 Must-Read Articles and Books About Next-Gen AI in 2025

Machine Learning Techniques

You could call it the best kept secret for professionals and experts in AI, as you won’t find these books and articles in traditional outlets. Yet, they are read by far more people than documents post...

Universal Dataset to Test, Enhance and Benchmark AI Algorithms

Machine Learning Techniques

This scientific research has three components. First, my most recent advances towards solving one of the most famous, multi-century old conjectures in number theory. One that kids in elementary school...

LLM Challenge with Petabytes of Data to Prove Famous Number Theory Conjecture

Machine Learning Techniques

In my recent article “Piercing the Deepest Mathematical Mystery” posted here, I paved the way to proving a famous multi-century old conjecture: are the digits of major mathematical constant such as π,...

Piercing the Deepest Mathematical Mystery

Machine Learning Techniques

Any solution to the problem in question has remained elusive for centuries. It is deemed more difficult than proving the Riemann Hypothesis, yet its formulation can be understood by kids in elementary...

10 Tips to Design Hallucination-Free RAG/LLM Systems

Machine Learning Techniques

Here I explain how we manage to avoid hallucinations with our home-made Enterprise RAG/LLM. The most recent article on the topic is available here. We do it with no training and zero parameter. By zer...

Blueprint: Next-Gen Enterprise RAG & LLM 2.0 – Nvidia PDFs Use Case

Machine Learning Techniques

In my most recent articles and books, I discussed our radically different approach to building enterprise LLMs from scratch, without training, hallucinations, prompt engineering or GPU, while deliveri...