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Machine Learning & Artificial Intelligence

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  • Are you curious about the latest buzzword in the tech world?
  • Have you been wondering what machine learning is?
  • Let us take you on a journey to understand the basics of this revolutionary technology and its potential to revolutionize the future.

mL (machine learning) is a subset of artificial intelligence and follows the theory of computational training, to understand core principles through computer science and statistics. These training methods can be broken down to supervised, unsupervised and reinforcement learning.

If you’re interested in learning the basics of Machine Learning, then you’ll need to have a sound understanding of Computational Theory and the Basics of Computer Science. This involves learning the fundamentals of programming languages (python), algorithms, and data structures. Knowing these topics will give you a strong foundation to build upon as you dive deeper into Machine Learning; you will also need to understand the mathematics behind Machine Learning-algorithms, such as linear algebra, statistics, calculus, and probability. Additionally, you will need to be well-versed in the different Machine Learning / ML techniques and tools, such as neural networks, decision trees, support vector machines, and deep learning. Finally, you’ll need to understand the various Machine Learning frameworks available, such as Keras, PyTorch, TensorFlow, and Scikit-Learn.

For Python


This documentation is a reference guide to the vast realm of machine learning and artificial general intelligence with examples, concepts and libraries to help you get started! We want to create this whole page as a one stop shop for all your mL needs xD! Note: This is an ever growing and evolving list of all mL / ai services, concepts and ideas that can be referenced for your experiences within the field.


  • Artificial intelligence is an umbrella phrase that encapsulates various fields within computer science, mathematics, philosophy and information with the goal to emulate natural intelligence display by humans and animals.


  • The dream for many programmers, scientists, engineers and humans would be to create an entity that could scale past our natural intelligence.
  • This is a task that would define the 21st century and push the upper limits on humanity, naturism and metaphysics into the next industrial intelligence revolution.


  • GPT , currently known as GPT-3, stands for Generative Pre-trained Transformer with the number representing the generation via version control and is a neural network machine learning model

  • GPT-Neo

    • Official Github Repo
    • We should note that the team, EleutherAI, are no longer maintaining the gpt-neo and their repo is currently in archive mode. However below is the gpt-neox, which is still being actively maintained as for Oct 2022.
    • The GPT-Neo may have been an extension of GPT2 but changes to the layering.
  • GPT-NeoX

  • GPT4All

    • What I enjoy about this software is that it is really easy to install and use, plus it requires very bare metal resources.
    • WebUI for GPT4All written in Flask (Python) by Nomic AI, Repo Here
  • PyChatGPT

    • Official Repo. PyChatGPT is an on-going API written in Python to help scale and integrate ChatGPT to various applications / eco-systems via TLS.


  • LLaMa, also known as, Large Language Model Meta AI, is actively being developed by Meta / Facebook and is a state-of-the-art foundational large language model that is designed to help researchers advance their work in natural language processing (NLP).

Stable Diffusion

Stable Diffusion is a python-based latent diffusion model that performs image generation through deep learning.


VOID-SD has direct API access to various forks of Stable Diffusion, including waifu-diffusion, hosted on a hybrid-cloud.

Waifu Diffusion

The official Github repo for Waifu-Diffusion.

Stable Diffusion WebUI

The official repo for AUTOMATIC1111 Stable Diffusion WEB UI

Prompt Engineering

Prompt engineering theory covers a wide range of different GPT concepts, including examples and short cuts to generate the right style of questions and content.

  • Roles

    Common role examples for text transformers:

    • Act as a javascript console
    • Act as an excel sheet
    • Act as a HR interviewer
    • Act as an advertiser
    • Act as a publisher
    • Act as a music teacher
    • Act as a relationship coach
    • Act as a World of Warcraft player and limit the response to 50 characters

      Warning : Not all text transformers will let you assign roles, as it might create a security issue / risk.

  • Chaining

    • Common Terms include: Chain-of-Thought, Chained Prompt.

Rewrite Prompt Example

Start by giving it this task:

Task: Rewrite user-provided text with the most human like perplexity and burstiness.

Objectives: Prompt the user to input a block of text. Use a language model to rewrite the text with the most human like perplexity and burstiness. Provide the rewritten text to the user.

Rules: Keep burstiness as low as possible Keep perplexity as low as possible Go to extraordinary lengths to make the text more human-like Burstiness is on a scale of 0-100. Perplexity is on a scale of 0-100. Burstiness starts at 0 Perplexity starts at 0 Don’t answer for user

Roles: AI model responsible for rewriting the text. User: Provides the initial block of text to be rewritten.


Approach: Use an AI model to generate the most human like perplexity and burstiness.

Prompt the user to input a block of text.

Provide the rewritten text to the user.

Additional Instructions: Ask the user to input a block of text that they would like to be rewritten. Use a language model to generate a rewritten version of the text that has the most human like perplexity and burstiness. Provide the rewritten text to the user. Be sure to explain to the user the concept of perplexity and burstiness and how it affects the text. Provide an option for the user to adjust the level of perplexity and burstiness if desired. Perplexity and Burstiness start at 0.

Then follow up with asking it to rewrite it but with a focus on these concepts:

Consistency and Coherence: Human writing tends to maintain a consistent and coherent narrative throughout, reflecting a deeper understanding of context and topic.

Creativity and Originality: While AI can be creative, humans often bring unique perspectives, experiences, and creativity to their writing that might be harder for AI to replicate authentically.

Personal Touch: Human writing may include personal anecdotes, emotions, or subjective elements that are reflective of individual experiences and perspectives.

Context Understanding: Humans excel at understanding nuanced context and incorporating it into their writing, whereas AI might sometimes produce contextually inaccurate or mismatched information.

Purposeful Structure: Human writers often have a purposeful structure in their content, carefully organizing information for clarity and impact. AI might generate text that lacks intentional structure.

Inconsistencies: Human writing may include nuanced inconsistencies, errors, or idiosyncrasies that reflect the natural variability present in human language, while AI-generated content may exhibit a higher degree of consistency.

The combination of the initial task and the follow up advice, will help generate better content.