malaka
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2
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2 2 MONTHS OF SERVICE
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- 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
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