Q: Generate an RDF-Turtle based glossary, from the text that follows, using the following guidelines:

1. Use terms such as schema:DefinedTerm from Schema.org
1. Denote subjects and objects using relative hash-based hyperlinks using @base set to https://medium.com/@mtrajan/absolute-beginners-guide-to-making-sense-of-key-ai-terms-in-2023-4ec9c2824c36#
1. Cross reference relevant DBpedia concepts where such exist
1. Output response to a code-block
1. Place `## Turtle Start ##` and `## Turtle End ##` around the code within the code-block

## Machine Learning

Machine learning is the technique or art that enables computer systems to automatically learn and improve from experience without being explicitly instructed or programmed. Instead of machines getting instructions they receive the observation of both input and output and are expected to learn from that.

Think of machine learning as the process of learning cooking. Just like there are different cuisines, different ways of **cooking** there are different models of machine learning.

There are 3 types of machine learning

* Supervised Learning
* Unsupervised Learning
* Reinforcement Learning

Supervised Learning

Supervised learning can be thought of as learning how to cook a dish under the guidance of a mentor who provides them with feedback on their cooking

Unsupervised Learning

Unsupervised learning can be thought of as a chef experimenting with ingredients to discover new flavor combinations without a recipe or mentor’s guidance

Reinforcement Learning

Reinforcement learning can be thought of as a chef learning how to cook a dish by repeatedly trying different techniques and receiving and getting rewards on punishments

## NLP — Natural Language Processing

NLP is the term used to refer to a subset of machine learning scope that is limited to when ingredients of learning are only in the natural language used by humans.

## Machine Learning Model

Machine learning models are different from machine learning. If machine learning is the art of teaching a computer system how to cook a new recipe. Machine learning model would be a specific recipe for a dish that has been developed and refined over time.

## Neural Networks

A neural network is a type of machine learning model that is inspired by the structure and function of the human brain.

Imagine you are trying to teach a group of chefs how to recognize different ingredients based on observation. A neural network is like organizing these chefs into several rows or teams, where each chef is responsible for recognizing specific characteristics. The first row team might identify basic features like portions, look/color, or way it should look. The second row team uses the first team’s findings to recognize more complex features, like texture or fragrance. Each subsequent team identifies even more intricate details based on the previous teams’ work. The final team makes the decision about what ingredient they’re looking at.

In this cooking analogy, each chef is like a neuron, and their decisions are based on the information they receive from the previous row. Neurons in a neural network receive input, process it, and pass the result to the next layer. Knowledge from each row is called as weights, and it determines the importance of each input. During the learning process, these weights are adjusted to minimize the error between the network’s predictions and the actual outcomes. All the chefs have to talk to each other based on what they have learnt by looking at the output given during the training

## Deep Learning

Deep learning is a subset of neural networks (machine learning model) that uses many rows i.e layers (hence, “deep”) to learn and model more complex features and patterns. If machine learning is cooking, deep learning is advanced cooking. It is believed that any complex problem of learning can be solved by increasing the number of rows (layers) in a neural network. Up until 2012 it was believed that this can’t be done in a practical way. Computer researchers Ilya Sutseveker, Geoff Hinton, Andrej Karpathy created a breakthrough here in one of their [research](https://medium.com/@mtrajan/absolute-beginners-guide-to-making-sense-of-key-ai-terms-in-2023-4ec9c2824c36) work named ImageNet that unlocked floodgates in machine learning techniques. Their research work suggested that if the rows or layers are organized in a specific way, if a special type of computer called GPU is used then computers can learn to see and visualize objects similar to how a baby does . This field of asking computers to see is called computer vision. Deep learning created a breakthrough in computer vision.

A:

## Turtle Start ##

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@prefix schema: <http://schema.org/>.
@prefix dbpedia: <http://dbpedia.org/resource/>.

# Machine Learning
<#MachineLearning> a schema:DefinedTerm ;
    schema:name "Machine Learning" ;
    schema:description "Machine learning is the technique or art that enables computer systems to automatically learn and improve from experience without being explicitly instructed or programmed." ;
    schema:inDefinedTermSet dbpedia:Machine_learning .

# Types of Machine Learning
<#SupervisedLearning> a schema:DefinedTerm ;
    schema:name "Supervised Learning" ;
    schema:description "Supervised learning can be thought of as learning how to cook a dish under the guidance of a mentor who provides them with feedback on their cooking." ;
    schema:inDefinedTermSet dbpedia:Supervised_learning .

<#UnsupervisedLearning> a schema:DefinedTerm ;
    schema:name "Unsupervised Learning" ;
    schema:description "Unsupervised learning can be thought of as a chef experimenting with ingredients to discover new flavor combinations without a recipe or mentor’s guidance." ;
    schema:inDefinedTermSet dbpedia:Unsupervised_learning .

<#ReinforcementLearning> a schema:DefinedTerm ;
    schema:name "Reinforcement Learning" ;
    schema:description "Reinforcement learning can be thought of as a chef learning how to cook a dish by repeatedly trying different techniques and receiving and getting rewards on punishments." ;
    schema:inDefinedTermSet dbpedia:Reinforcement_learning .

# NLP
<#NLP> a schema:DefinedTerm ;
    schema:name "NLP" ;
    schema:description "NLP is the term used to refer to a subset of machine learning scope that is limited to when ingredients of learning are only in the natural language used by humans." ;
    schema:inDefinedTermSet dbpedia:Natural_language_processing .

# Machine Learning Model
<#MachineLearningModel> a schema:DefinedTerm ;
    schema:name "Machine Learning Model" ;
    schema:description "Machine learning models are different from machine learning. If machine learning is the art of teaching a computer system how to cook a new recipe. Machine learning model would be a specific recipe for a dish that has been developed and refined over time." ;
    schema:inDefinedTermSet dbpedia:Machine_learning_model .

# Neural Networks
<#NeuralNetworks> a schema:DefinedTerm ;
    schema:name "Neural Networks" ;
    schema:description "A neural network is a type of machine learning model that is inspired by the structure and function of the human brain." ;
    schema:inDefinedTermSet dbpedia:Artificial_neural_network .

# Deep Learning
<#DeepLearning> a schema:DefinedTerm ;
    schema:name "Deep Learning" ;
    schema:description "Deep learning is a subset of neural networks (machine learning model) that uses many rows i.e layers (hence, 'deep') to learn and model more complex features and patterns." ;
    schema:inDefinedTermSet dbpedia:Deep_learning .

## Turtle End ##

Q: Repeat for the following. 

## Transformers

A Transformer is a machine learning model. Think of them as chefs, not just a refined recipe. They are designed to understand and process sequences or combinations of information. The previous machine learning model never took context into account. Earlier popular ones were always looking at inputs in a sequence one at a time. This is the first model that takes context into account

To learn this in a way, the machine has to understand the relationships and dependencies between different ingredients and techniques. Transformer model learns to understand the relationships and dependencies between words or phrases in a text that is fed to it.

This model was released through a paper “Attention is all you need” and has been the most revolutionary research work of the last 5 years. This model has beaten every other model and also works for a wide variety of domains. It can do language translation, can answer questions, and do computer vision tasks. Researchers are still finding out that there are many artificial intelligence tasks that a transformer can handle. The reason it is so revolutionary is due to the general purpose applicability of this.

## Back Propagation Algorithm

Back propagation is the most popular algorithm (step by step instructions) given to a computer which is running a machine learning model such as a neural network.

Back propagation refers to the technique in which the recipe improves or where a chef learns. Using the inputs (or ingredients) an output (or dish) is created for diners. After tasting the dish, the diners provide feedback on the dish’s quality, such as flavor, texture, or presentation.

In a neural network, this feedback comes in the form of a loss function, which measures the difference between the model’s output and the expected output (ground truth).

Based on the feedback, the chef identifies the aspects of the dish that need improvement, such as adjusting ingredient quantities or altering the cooking time.

This feedback he memorizes in his mind, that memory is called as weights.

The chef continues to receive feedback and make adjustments to their techniques and recipes until the diners are satisfied with the dish

## Token

A token is a unit of text that represents a single meaningful element, such as a word, subword or a punctuation mark. Tokens are created by breaking down sentences or larger text into smaller, more manageable pieces for analysis, processing, or generating new text.

Using the cooking metaphor, tokens can be thought of as the individual ingredients that make up a recipe. Just as a recipe is a combination of various ingredients, a sentence or text is a combination of tokens. Each token contributes to the overall meaning or structure of the text, just as each ingredient contributes to the flavor and texture of a dish.

Tokenization is the process of converting a text into tokens, which involves splitting the text based on specific rules or criteria, such as spaces, punctuation marks, or other delimiters.

## Tuning

Tuning, also known as fine-tuning, is a step in the machine learning process where a pre-trained model is further trained on a specific dataset or task to improve its performance and adapt to the nuances of the new data or problem. This process helps the model to become more specialized and proficient in the target task or domain.

Continuing with the cooking metaphor, imagine a chef who has a broad culinary background and general cooking skills. Tuning is like providing the chef with **special training** in a specific cuisine or technique to refine their expertise in that area. It is like a master chef who is training in Italian cuisine.

Tuning is a common practice in machine learning, as it allows researchers and developers to leverage the capabilities of pre-trained models and customize them to address specific tasks or problems, saving time and resources compared to training a new model from scratch.

## Transfer Learning

Transfer Learning is like a chef leveraging their existing culinary knowledge and skills to quickly adapt to a new cuisine or cooking style. Building on their foundational knowledge from pre-training, the chef can apply their understanding of techniques, ingredients, and flavor combinations to create dishes in the new cuisine.

A:
## Turtle Start ##

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# Transformers
<#Transformers> a schema:DefinedTerm ;
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    schema:description """A Transformer is a machine learning model designed to understand and process sequences or combinations of information. It takes context into account and can handle a wide variety of domains, including language translation, question answering, and computer vision tasks.""" ;
    # schema:inDefinedTermSet dbpedia:Transformer_(machine_learning_model) .
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# Back Propagation Algorithm
<#BackPropagationAlgorithm> a schema:DefinedTerm ;
    schema:name "Back Propagation Algorithm" ;
    schema:description "Back propagation is the most popular algorithm given to a computer which is running a machine learning model such as a neural network. It involves adjusting weights based on the feedback from a loss function, which measures the difference between the model's output and the expected output (ground truth)." ;
    schema:inDefinedTermSet dbpedia:Backpropagation .

# Token
<#Token> a schema:DefinedTerm ;
    schema:name "Token" ;
    schema:description "A token is a unit of text that represents a single meaningful element, such as a word, subword or a punctuation mark. Tokenization is the process of converting a text into tokens, which involves splitting the text based on specific rules or criteria, such as spaces, punctuation marks, or other delimiters." ;
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    schema:inDefinedTermSet <http://dbpedia.org/resource/Tokenization_(natural_language_processing)> .

# Tuning
<#Tuning> a schema:DefinedTerm ;
    schema:name "Tuning" ;
    schema:description "Tuning, also known as fine-tuning, is a step in the machine learning process where a pre-trained model is further trained on a specific dataset or task to improve its performance and adapt to the nuances of the new data or problem. It helps the model to become more specialized and proficient in the target task or domain." ;
    # schema:inDefinedTermSet dbpedia:Fine-tuning_(machine_learning) .
    schema:inDefinedTermSet <http://dbpedia.org/resource/Fine-tuning_(machine_learning)> .

# Transfer Learning
<#TransferLearning> a schema:DefinedTerm ;
    schema:name "Transfer Learning" ;
    schema:description "Transfer Learning is the process of leveraging existing knowledge and skills from a pre-trained model to quickly adapt to a new task or domain, allowing researchers and developers to save time and resources compared to training a new model from scratch." ;
    schema:inDefinedTermSet dbpedia:Transfer_learning .

## Turtle End ##

Q: Repeat for the following. 

## Pre-training

Pre-training is like a chef’s foundational culinary education and early experiences. during pre-training, a model like GPT learns the general structure of language, grammar, syntax, and some factual knowledge by processing a massive corpus of text from diverse sources.The word P in GPT stands for pre-training

## Fine-tuning

Fine-tuning is the process of adjusting the chef’s techniques, experimenting with new ingredients, or altering cooking times to adapt their skills to the new cuisine

fine-tuning refers to the process of adapting the pre-trained model’s parameters using a smaller, task-specific dataset. This enables the model to generate more accurate and relevant outputs for the new task, building upon the general knowledge it acquired during pre-training.

Training Dataset

Training data set is like a recipe book that chef learns from, providing a structured guide for the chef to follow and learn new techniques and flavor combinations. In machine learning, the training dataset is a set of labeled examples that teach a model how to make accurate predictions by identifying patterns and relationships between the input features and the output labels.

## LLM — Large Language Mode

LLM stands for Large Language Model. LLMs are deep neural networks (machine learning model) built specifically for language learning. Due to the popularity of Transformers, most LLMs are based on Transformer.s To continue with the cooking metaphor, an LLM can be thought of as a master chef who has studied and practiced countless recipes, techniques, and cuisines. This vast experience enables the master chef to understand and create a wide range of dishes, adapt to new ingredients, and innovate by combining different culinary elements.

So there is the art of cooking (machine learning), advanced cooking, refined recipe books (machine learning models), chefs (transformers) and LLMs (master chef).

## Generative

The general expectation of the machine learning model has been that it will correctly identify what it has learnt through the enormous data set that it has trained on. Now when a model is able to express things which are not explicitly in the training set then it is said to be generative. It is like a chef who creates a new dish based on their knowledge and understanding of various ingredients and techniques.

## GPT

GPT stands for Generative Pre-trained Transformer.

Transformer — A transformer is a LLM i.e large language model which means it is primarily built for language learning tasks. An LLM is a deep neural network. A neural network is a machine learning model. Unique aspect of this model is that it takes context into account when taking an input text, it recognises that the position of a word in a sentence changes its meaning. Transformer is like a master chef it is so powerful that it can learn and do so many different cuisines

Pre-training — stands for learning from a large dataset, much like a chef attending culinary school. During this stage, the chef learns fundamental cooking techniques, basic recipes, and the characteristics of various ingredients.

Generative — When a model is able to express things which are not explicitly in the training set then it is said to be generative. It is like a chef who creates a new dish based on their knowledge and understanding of various ingredients and techniques.

GPT had the most unprecedented adoption in the history of technology. It reached 100m users in less than 3 months.

A:
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# Pre-training
<#PreTraining> a schema:DefinedTerm ;
    schema:name "Pre-training" ;
    schema:description "Pre-training is a stage in the machine learning process where a model, like GPT, learns the general structure of language, grammar, syntax, and some factual knowledge by processing a massive corpus of text from diverse sources. It is like a chef's foundational culinary education and early experiences." ;
    schema:inDefinedTermSet dbpedia:Pre-training .

# Fine-tuning
<#FineTuning> a schema:DefinedTerm ;
    schema:name "Fine-tuning" ;
    schema:description """Fine-tuning is the process of adapting a pre-trained model's parameters using a smaller, task-specific dataset. This enables the model to generate more accurate and relevant outputs for the new task, building upon the general knowledge it acquired during pre-training.""" ;
    # schema:inDefinedTermSet dbpedia:Fine-tuning_(machine_learning) .
    schema:inDefinedTermSet <https://dbpedia.org/resource/Fine-tuning_(machine_learning)> .

# Training Dataset
<#TrainingDataset> a schema:DefinedTerm ;
    schema:name "Training Dataset" ;
    schema:description "A training dataset is a set of labeled examples that teach a machine learning model how to make accurate predictions by identifying patterns and relationships between input features and output labels. It is like a recipe book that a chef learns from, providing a structured guide for learning new techniques and flavor combinations." ;
    schema:inDefinedTermSet dbpedia:Training_set .

# LLM — Large Language Model
<#LargeLanguageModel> a schema:DefinedTerm ;
    schema:name "Large Language Model (LLM)" ;
    schema:description "LLMs are deep neural networks built specifically for language learning. Most LLMs are based on Transformers. In the cooking metaphor, an LLM can be thought of as a master chef with vast experience, enabling them to understand and create a wide range of dishes, adapt to new ingredients, and innovate by combining different culinary elements." ;
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# Generative
<#Generative> a schema:DefinedTerm ;
    schema:name "Generative" ;
    schema:description "A generative machine learning model is capable of expressing things that are not explicitly in the training set. It is like a chef who creates a new dish based on their knowledge and understanding of various ingredients and techniques." ;
    schema:inDefinedTermSet dbpedia:Generative_model .

# GPT
<#GPT> a schema:DefinedTerm ;
    schema:name "GPT (Generative Pre-trained Transformer)" ;
    schema:description "GPT stands for Generative Pre-trained Transformer, a powerful LLM primarily built for language learning tasks. It combines the Transformer's context-aware capabilities with pre-training and generative properties. GPT had the most unprecedented adoption in the history of technology, reaching 100 million users in less than 3 months." ;
    schema:inDefinedTermSet dbpedia:OpenAI_GPT .

## Turtle End ##

Q: Repeat for the following. 

## GPT, T5, BART

They are all large language models (i.e master chef) from different large companies. They also different slightly in their approach.

GPT is the master chef from the house of OpenAI

BART is the master chef from the house of Facebook.

T5 is the master chef from the house of Google.

The difference among those three is that GPT is “free style” cooking master chef, T5 is “fusion cooking” and BART is a “precision cooking” master chef. Because of these differences each is good at doing one thing better over the other. GPT is the most popular of them all.

## ChatGPT

If GPT is the master chef with broad expertise in text generation, ChatGPT is the specialized personal chef who engages in conversations with customers to cater to their specific needs. Together, they form a team that can produce engaging, contextually relevant, and personalized dialogues for users.

## Parameters

This term is often interchangeably used with the term. weights. When you look at the back propagation definition you would notice that the process of learning involves collecting feedback and making adjustments on ingredients based on the feedback. The memory of adjustment is called weights. The more memory the neural network has learnt. GPT2 had $1.5 billion parameters (or memory points), GPT1 has 117 million, GP2 has 1.5 billion, GPT3 has 175 billion while GPT 4 has 170 trillion parameters. *This is not to be confused with memory that GPT 3 and GPT 4 has.*

Have explicitly left defining few foundational unit such as perceptron, expert systems, decision trees, support vector machine, recurrent neural network (RNN), convolutional neural network (CNN), generative adversarial network (GAN), long term short memory (LTSM) which is important to understand things from historical and evolution perspective.

![|700x433](https://miro.medium.com/v2/resize:fit:700/0*eqvFuVL9o29J0OI8)

Metaphors are never perfect. They are like this rickety sofa in your brain on which you can seat a more complex understanding. Now that you have this rickety sofa for machine learning and AI go forward to read more things to improve your understanding.

A:
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# GPT, T5, BART
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    schema:description "GPT, T5, and BART are large language models (master chefs) from different large companies, with distinct approaches. GPT is the 'free style' cooking master chef from OpenAI, T5 is the 'fusion cooking' master chef from Google, and BART is the 'precision cooking' master chef from Facebook. GPT is the most popular of them all." ;
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    schema:inDefinedTermSet <http://dbpedia.org/resource/Transformer_(machine_learning_model)> .

# ChatGPT
<#ChatGPT> a schema:DefinedTerm ;
    schema:name "ChatGPT" ;
    schema:description "If GPT is the master chef with broad expertise in text generation, ChatGPT is the specialized personal chef who engages in conversations with customers to cater to their specific needs. Together, they form a team that can produce engaging, contextually relevant, and personalized dialogues for users." ;
    schema:inDefinedTermSet dbpedia:OpenAI_ChatGPT .

# Parameters
<#Parameters> a schema:DefinedTerm ;
    schema:name "Parameters" ;
    schema:description "Parameters, often used interchangeably with the term 'weights,' are the memory points in a neural network that store the adjustments made based on feedback during the learning process. GPT1 has 117 million parameters, GPT2 has 1.5 billion, GPT3 has 175 billion, and GPT4 has 170 trillion parameters. This is not to be confused with memory capacity." ;
    schema:inDefinedTermSet dbpedia:Machine_learning_parameters .

## Turtle End ##