Let’s build and train Chat GPT from scratch — no deep learning experience required
1 — Introduction: Deep Learning and NLP
In today’s digital age, we find ourselves conversing with machines more often. Whether you’re asking your smartphone about tomorrow’s weather or translating a webpage into your native language, behind this all lies the fascinating domain of Deep Learning and Natural Language Processing (NLP).
If you have little to no background in deep learning (or NLP), but wish you did, then this series is for you. I will go step by step, starting with basic concepts and building to a working ChatGPT model. It would be beneficial to know the basics of Python — if you’re lacking that, then go somewhere for 1 hour, learn Python, and then come back :-)
I will do my best to spell things out every step of the way, but please let me know if any particular part of this series could use a revision. My personal goal is to make the world of deep learning and NLP less scary to those of us who don’t have a background in ML.
For completion, let’s wrap this introduction up by talking about what deep learning and NLP are. Then, let’s start building!
Deep Learning, A Quick Primer: Deep learning is a subset of machine learning where algorithms are inspired by the structure and function of the human brain. Imagine having an artificial brain with layers of interconnected nodes. You input some data into the brain (images, text, etc) and ask a question of it (what does this image contain, or what text is most likely to follow, respectively). As the data flows through nodes, mathematical operations are performed to transform the raw data into something else that we can interpret, thus helping us answer the original question. Some of these operations use stored numbers or weights. Building a successful brain, or as we call it, the model requires that we find weights that help to turn the input data into a value(s) that can help us better answer the original question. Still doesn’t make sense? Don’t worry. Keep reading, and I promise it will.
NLP, A Quick Primer: Natural Language Processing, or NLP, is a branch of artificial intelligence that focuses on the interaction between computers and humans through language. Its primary aim is to allow machines to understand, interpret, and generate human language in a way that is both meaningful and useful. Think of it as teaching machines to speak and understand our language.
Just as our brains are designed to pick up on nuances, sarcasm, and context in communication, NLP strives to enable machines to do the same. This involves various tasks such as translation, sentiment analysis, and chatbot functionalities, to name a few.
For instance, when you ask a virtual assistant like Siri or Alexa a question, it uses NLP to interpret your words, understand the context, and generate a response. The challenges lie in understanding the vast complexities of human language, including idioms, cultural contexts, and ambiguities.
What’s next?
Understanding how to build and train deep learning models for NLP at the highest level takes years of academic study and hands-on experience. It’d be impossible to create a substitute for those, but I aim to do the next best thing. In this series, we’ll build an extremely relevant and popular (in 2023) deep learning model (one variant, at least) — the transformer. Transformers are the technology behind Chat GPT — if you understand how they work, you’ll have a solid understanding of how Chat GPT works as well.
We will start at the very beginning. Before we even mention transformers or GPT, we’ll have to spend some time on smaller components that will come together to allow us to build increasingly complex models. Because I’ll be assuming that no topic is too “beginner”, there may be some posts that you can skip through. If you happen to skip through something but realize later that you shouldn’t have, I’ll always try to include backlinks to help get you back on track.
Let’s get started!
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For more insights on Artificial Intelligence and related topics, check out: The History of AI, The Fundamentals of AI, Natural Language Processing (NLP), AI for Smart Cities, The Ethics of AI, AIs Carbon Footprint, AI Model Bias, Neural Networks, AI in Biology, AI in Healthcare, Generative Adversarial Networks, Quantum Artificial Intelligence, Evolutionary Algorithms, Genetic Algorithms, Robotics and AI, AI in Finance, AI in Education, AI in Agriculture, Reinforcement Learning, AI & Art, Using AI to Enhance Customer Experience, Speech-to-Text AI, How AI Is Transforming The Music Industry, Text-to-Speech AI, Robotic Sanitation, Supervised vs Unsupervised Learning, and Computer Vision.
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