The Future Of Software engineering and AI: What YOU can do about it
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Lessons
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Introduction to AI-Centric EvaluationAI BootcampLearn metrics and evaluation design for LLMs, laying the foundation for future metrics work.
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Building Your First LLM ApplicationAI BootcampCompare architecture types (generic LLMs, ChatGPT, domain-specialized), use tools like Hugging Face and Modal, and build a “Hello World” LLM inference API with OPT-125m.
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LLM Inference BasicsAI BootcampUnderstand inference (prompt processing vs autoregressive decoding), explore real-world AI applications, and learn the five phases of the model lifecycle (pretraining to RLHF to evaluation).
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Limitations of LLMsAI BootcampIdentify LLM limitations (hallucinations, lack of reasoning, prompt sensitivity) and learn from real-world startup examples (e.g., AutoShorts, HeadshotPro).
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LLM Use Cases Across IndustriesAI BootcampExplore LLM applications in healthcare, finance, legal, etc., and discuss project scoping (what to build vs outsource, viable ideas).
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Understanding LLM Projects and ModalitiesAI BootcampCompare transformer-based LLMs vs diffusion models, explore LLM components (prompts, embeddings, generation, inference), and understand application types (RAG, vertical models, agents, multimodal).
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Probability and Tensor BasicsAI Bootcamp- Intro to probability, distributions, classification vs regression - Tensor intuition and compute providers (GPU, Colab, cloud vs local)
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Statistics and Data PreprocessingAI Bootcamp- Exploring core stats: mean, standard deviation, normal distributions - Working with real datasets (Titanic) using Pandas: filtering, grouping, feature engineering, visualization - Preprocessing tabular data for ML: encoding, scaling, train/test split
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Mathematical Foundations for MLAI Bootcamp- Exponentiation and logarithms: visual intuition and matrix operations - Normalization techniques and why they matter in ML workflows - Activation functions: sigmoid and softmax with coding from scratch
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Arrays, Vectors & Tensors in Practice for Foundational KnowledgeAI Bootcamp- Creating and manipulating 2D and 3D NumPy arrays (reshaping, indexing, slicing) - Performing matrix operations: element-wise math and dot products - Visualizing vectors and tensors in 2D and 3D space using matplotlib
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Python, Google Colab & Jupyter Notebooks for AIAI Bootcamp- Jupyter & Python Setup - Understanding why Python is used in AI (simplicity, libraries, end-to-end stack) - Exploring Jupyter Notebooks: shortcuts, code + text blocks, and cloud tools like Google Colab
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Setting Up Accountability and ToolsAI Bootcamp- Meet the instructors and understand the support ecosystem (Circle, Notion, async help) - Learn the 4 learning pillars: concept clarity, muscle memory, project building, and peer community - Understand course philosophy: minimize math, maximize intuition, focus on real-world relevance - Set up accountability systems, learning tools, and productivity habits for long-term success