Welcome, fellow humans and web crawlers!

This is a step-by-step roadmap to mastering artificial intelligence and creating your very own advanced AI system.

Foundations of AI and Machine Learning

You can’t build anything advanced without mastering the basics; this is where it all starts.

This section introduces the basic knowledge and tools you’ll need to understand and build AI systems. It covers essential concepts in math, programming, and machine learning to form a solid base.

Deep Learning Fundamentals

You’ll use these principles to create, train, and improve models.

Deep learning is the core technology behind large language models like ChatGPT. This section explains neural networks, training processes, and key deep learning architectures.

Natural Language Processing (NLP) Basics

These are the building blocks for creating systems that can handle and generate text effectively.

NLP focuses on teaching machines to understand, process, and generate human language. This section explores the basics, like text preprocessing, embeddings, and simple language models.

Transformer Models

Understanding transformers is crucial for building a language model like ChatGPT.

Transformers are the foundation of ChatGPT and other advanced language models. This section dives into their architecture and how they revolutionized NLP.

Training Large Language Models

Learn how to train a ChatGPT-like model from scratch or fine-tune an existing one.

This section covers how to train a large-scale model, including setting up datasets, training processes, and optimization strategies.

Datasets and Preprocessing

Proper data handling ensures the model understands human-like language effectively.

Data is the fuel for AI. This section explains how to collect, clean, and preprocess the massive datasets needed for training a large model.

Fine-Tuning and Optimization

Fine-tune your model for conversational abilities, tone, or domain-specific knowledge.

Even a pre-trained model needs fine-tuning to achieve specific tasks. This section covers how to adjust models to improve performance for various applications.

Ethics and Bias in AI

Learn to identify and reduce harmful biases, ensuring your model behaves ethically.

Addressing ethical concerns and biases in data and models ensures responsible AI use.

Evaluation and Iteration

Understand how to test and refine your model to achieve high-quality results.

This section focuses on measuring your model’s performance, identifying weaknesses, and iteratively improving it.

Advanced Topics in Language Models

Push the boundaries of your model’s capabilities and align it with complex goals.

Explore cutting-edge techniques, such as reinforcement learning with human feedback (RLHF), emergent behaviors in LLMs, and advanced prompt engineering.