The Path to become a Transformer Guru

Last updated on March 17, 2024

Through my experience and research AI Guru, I've designed a learning path to become for a learner to become a Transformer Guru.

This article is my AI Guru road map to help you become a Transformer Guru. It is a guide to the resources to take you to the next level. Follow all of the steps with determination and you will become a leading new-era AI, NLP, and Transformer model guru!

Become a Transformer Guru in 7 steps:

I.Computational Linguistics

II.Cognitive Science

III.Prompt Engineering, no coding

IV.Transformer Architecture( thus mathematics)

V.Development (coding)

VI.Integrating Transformers in a Pipeline

VII.Explore New Perspectives

Please message me any questions you wish here on LinkedIn during your learning journey.??

I. Computational Linguistics

Large Language Models(LLM) are natural language processing algorithms. Natural Language Processing is a branch of computational linguistics. It is important to know the main concepts of computational linguistics before entering the realm of transformer models.

II. Cognitive Science

Cognitive Science is a must if you want to explore NLP deeper and obtain a broader view of how humans reason and apply it to machine learning. You need to understand the basic concepts of cognitive science whether you are a project manager, a developer, or wish to interact efficiently with a model.

Take your time to understand Cognitive Science from all perspectives.

If you have a wide cognitive science perspective (including mathematics), you can acquire a high level of knowledge on transformers with no coding and go further with coding.

III. Prompt Engineering, No Coding

The first step is to understand how transformers are built and how to use them with no coding. Engineer prompting is a new skill that you can develop with no coding.

The key is to learn the metalanguage of the NLP task you wish to use. A metalanguage is a language that describes another language. In this case, the metalanguage you will describe is the language you need to use to design prompts to obtain the best result possible.

The two leading sites to enhance your prompt skills are :

a) Google Search

b) the new Microsoft Bing

IV. Architecture (thus mathematics)

To fully understand the architecture of the Original Transformer model, read Chapters 1 and 2 of Transformers for Natural Language Processing and Computer Vision, 3rd Edition:

Now, take your time to read that chapter. Once you understand it, everything that follows will flow much easier. Take your time to dive into matrix multiplications and other AI math. It's a time saver!

You are now ready to explore the OpenAI GPT models

OpenAI GPT Models

Read the chapters pertaining to GPT models in Transformers for Natural Language Processing.

To test GPT models, sign up on OpenAI to gain access to ready-to-use examples.

Sign up and go straight to Transformers for NLP, 2nd Edition,2022, Denis Rothman to jump right into pragmatic GPT-3 examples. Just look for "GPT-3" with Ctrl-F, and you'll soon be a GPT-4, GPT-4o, and o1 aficionado.

Take your time also. Explore!

V. Development(Coding)

Remember, AI coding is supercharged when you have solid mathematical knowledge. Otherwise, it will take you longer than your competition for difficult topics.

Transformers for NLP contains many examples you can build. You can read the book and my posts and watch my videos for more.

Read chapters 2 and 3 of Transformers for NLP on how to train and fine-tune BERT models, BUT for more, you can fine-tune GPT-3! To see how you can view this video:

Once you know how to fine-tune one model, you have the knowledge and experience to expand your implementations to other frameworks.

VI. Integrating Transformers in a Pipeline

Building an application is a challenging task that involves standard project management and DevOps approaches. OpenAI ChatGPT Plus, version GPT-4, explains how to achieve this goal quite well(text generated by GPT-4; do not use this text without citing the source: "text generated by Denis Rothman through OpenAI GPT-4"):

"Integrating ChatGPT in a pipeline involving a project manager, data retrieval, prompt engineering, and fine-tuning a transformer model can be a highly efficient way to improve the quality of your AI model. Here's a step-by-step guide to achieve this integration:

1. Project management:

Start by defining the scope, objectives, and goals of the project. Assign a project manager to oversee the entire process, coordinate team members, allocate resources, and ensure the timely execution of tasks.

Keep product management (development and maintenance constraints, life cycle).

2. Data retrieval:

Identify the sources from which you will be collecting data. These sources may include APIs, databases, web scraping, or manual data collection. Create a data retrieval plan that specifies the data sources, the type of data needed, and the retrieval method. The project manager should ensure that the data retrieval process aligns with the project's goals and is completed in a timely manner.

3. Data preprocessing:

Once the data has been collected, preprocess it by cleaning, transforming, and normalizing the data. This step may involve removing duplicates, filling in missing values, and converting data into a consistent format. Preprocessed data should be stored in an organized and accessible manner, such as a database or a data lake.

4. Prompt engineering:

This step involves designing effective prompts to improve the quality of the ChatGPT model. Use the preprocessed data to create a set of input-output pairs that cover the desired range of topics and tasks. Make sure to include both positive and negative examples to help the model learn from a variety of situations. The project manager should coordinate with the team to ensure that the prompts are well-crafted and optimized for the specific use case.

5. Dataset preparation:

Split the preprocessed data and prompt pairs into training, validation, and testing sets. This allows you to train the model on one subset of data, validate its performance on another, and then test its final performance on a separate set. Ensure that the datasets are balanced and representative of the problem space.

6. Fine-tuning the transformer model:

With the datasets prepared, proceed to fine-tune the pre-trained ChatGPT model. Fine-tuning involves training the model on your custom dataset for a few epochs, allowing it to adapt to the specific task. Be cautious of overfitting – monitor validation loss and other relevant metrics to ensure the model generalizes well to new data.

7. Evaluation:

After fine-tuning, evaluate the performance of the ChatGPT model on the test dataset. Use appropriate evaluation metrics such as accuracy, F1-score, or custom metrics based on your project's objectives. Analyze the results to identify areas of improvement and iterate on the model if needed.

8. Deployment and monitoring:

Once satisfied with the model's performance, deploy the ChatGPT model in your desired application or service. Continuously monitor its performance and gather user feedback to identify any issues or areas for improvement. Periodically update the model with new data and fine-tune it to maintain its effectiveness.

Throughout this pipeline, the project manager plays a crucial role in coordinating the efforts of team members, managing resources, and ensuring that each step is executed efficiently and effectively. By following this process, you can successfully integrate ChatGPT into your pipeline and create a high-performing AI model for your specific use case."

VII. Explore New Perspectives

The following article provides all the resources you need to explore the present and future of transformer models including OpenAI, ChatGPT, GPT-4, DALL-E, and more.

Click and enter on the image and the new world of AI!

View the world with new eyes!