AI for Software Engineers
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Will Sentance
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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
Attend Online Live and in Full HD
Join-In Person from Downtown Minneapolis
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AI for Software Engineers
Event Details
What
- One Full Day Workshop Session
- Replay Videos (available immediately)
When
- September 19, 2024 - 9:30am to 5:30pm Central Daylight Time
Where
- Option 1: Attend online on our full HD live stream
- Option 2: Attend in-person at HQ in Minneapolis, MN
Daily Schedule
- 9:30AMProductizing Prediction: traditional ML approaches
- 10:30AMNeural Networks: data representation, weights & activation
- 11:30AMNeural Networks: Gradient Descent, Backpropagation & validation
- 12:30PMLunch
- 1:30PMLLMs: Tokenization, Embeddings, Self-Attention
- 3:30PMPrompting Heuristics
- 5:00PMHow Software Engineering teams build products with AI
Your (Awesome) Instructor
Will Sentance
Will Sentance is co-founder and CEO at Codesmith – a software engineering and machine learning residency based in Los Angeles, New York, and Oxford. He is the creator of Icecomm, the most famous developer platform for P2P video and data communication. Before Codesmith, Will was CEO at Ownly and a Software Engineer at Gem.
Will graduated from Oxford University and Harvard University.
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