Software engineers (and aspiring engineers) who want to understand the principles behind the latest AI models they’re incorporating into their products and workflows. Also, any engineers who want to stand out in interviews as the software engineer who, while not an ML engineer, can nevertheless offer significant value and insight for how to integrate ML/AI models.

The fullstack engineer (frontend, backend, infrastructure) has been augmented with a new component - prediction - from predicting user behavior to text & pixels - ‘generative’ AI.

To stand out as a fullstack software engineer in this era you need to begin developing an under-the-hood understanding of these new tools - particularly the ‘models’ at their heart - neural networks and transformers.

We’ll cover the nature of data, probability, training and prediction in Machine Learning. We’ll then explore the way these principles play out in neural networks used in deep learning including the core concepts of gradient descent and backpropagation.

We’ll then explore how and why to use large language models (LLMs) by understanding tokenization, embeddings, self-attention, pre-training and fine-tuning, as well as the heuristics necessary for reliable model prompting.

We’ll also explore how software engineering teams are evolving to incorporate this new part of the stack. With your first-principles understanding of the tools involved, you will be able to make informed judgments on how to integrate ML/AI models, speak to that in your teams and have an invaluable edge in tech interviews.