Navigating the Generative AI Learning Pathway: From Statistics to LLMs and Beyond
Are you interested in learning about generative AI but not sure where to start? In this blog post, we will discuss the steps to take in order to properly understand and master generative AI.
Firstly, it is important to note that using Large Language Models (LLMs) does not equate to learning generative AI. Many data scientists build applications based on LLMs, which can be beneficial for interpreting and generating human natural language. However, in order to truly understand generative AI, one must delve deeper into the science behind building LLMs.
One recommended pathway to learning generative AI starts with statistics for machine learning. This will provide you with a solid foundation in understanding descriptive and inferential statistics, as well as loss functions which are crucial in training LLMs. Cross entropy, a commonly used loss function in LLM training, involves comparing predicted and actual probability distributions of words.
Next, data exploration is essential for familiarizing yourself with datasets, both structured and unstructured. This will lead you into the realm of natural language processing (NLP), which is crucial for generative AI.
Understanding machine learning modelling techniques is also key in grasping how supervised models work and how they approximate datasets during the training process. By learning how to summarise and interpret datasets, you can better appreciate how a model predicts for unseen data.
Delving into deep learning and artificial neural networks (ANNs), specifically recurrent neural networks (RNNs) and transformers, will provide you with insights into how LLMs are built and used for content generation. Additionally, exploring the technology ecosystem around LLMs, such as LangChain, LM Studio, and RAG, can enhance your understanding of how to interact with and fine-tune LLMs.
Finally, the importance of storytelling when explaining your generative AI solutions to stakeholders cannot be stressed enough. Tailoring your explanations to different audiences, such as data science teams, managers, executives, customers, and business users, is crucial in ensuring understanding and acceptance of your work.
In conclusion, the journey to learning generative AI is complex and multifaceted. By following a structured pathway and remaining open to learning and new experiences, you can navigate this exciting field and make a meaningful impact. Remember to stay curious, persistent, and open-minded in your pursuit of generative AI knowledge.