AI for Software Engineers

Develop an under-the-hood understanding of the principles behind AI - neural networks, GPTs and LLMs - to stand out as the software engineer that can truly integrate these models into software to build new products, augment your workflows and solve the hardest business problems.

Key Takeaways

By participating along with us in the workshop, you'll learn:

• How fullstack engineering is evolving to incorporate prediction (ML/AI) into the stack
• How to use a first-principles understanding of the models involved to make informed judgments in your software engineering work and career
• How data science and ML are used to build products using classical models that don’t use neural networks
• The principles behind neural networks (the core tool of deep learning) - data representation, weights and activation, gradient descent and backpropagation
• How LLMs represent data through tokenization, embeddings, self-attention and the transformer architecture, and how this representation informs our decisions around how and why to use LLMs
• How LLMs are guided to generate text through pre-training and fine-tuning and how to interact with LLMs in the most effective and efficient way
• Which heuristics should guide our iterative process for prompting models to reliably produce our desired outputs
• What knowledge, skills and mindset shifts AI requires for the modern fullstack engineer and how they fit into AI-driven team structures

Is This Workshop for Me?

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.

Workshop Details

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.

Any Prerequisites?

Solid understanding of programming fundamentals in any programming language