AI for Software Engineers
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 8h 19m | 2.96 GB
Instructor: Will Sentance
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
