Master Large Language Models with Zero Code! Learn AI, Prompting & Fine-Tuning Through Fun & Tasty Food Analogies
What you will learn
Understand what large language models (LLMs) are and how they work using real-world analogies
Identify key ingredients that power LLMs, like training data, tokenization, and data quality.
Explain how LLMs are trained using concepts like batches, epochs, and loss functions.
Write better prompts using techniques like zero-shot, few-shot, and chain-of-thought.
Customize models using fine-tuning and tools like Hugging Face and LoRA.
Evaluate model performance using both quantitative and qualitative metrics.
Deploy LLMs using APIs, FastAPI/Flask, and host them on platforms like Hugging Face Spaces.
Build full LLM-powered applications using no-code tools and LangChain.
Monitor and improve your AI models using logs, feedback loops, and A/B testing.
Monitor and improve your AI models using logs, feedback loops, and A/B testing.
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- Culinary AI Foundations: Dive into the intricate “kitchen” of LLMs, demystifying their core components and operational mechanics through relatable food-based metaphors.
- The “Secret Sauce” of LLM Power: Uncover the essential elements that make LLMs tick, from the foundational “pantry” of training data to the precise “chopping” of tokenization and the critical “freshness” of data quality.
- The Art of “Baking” LLMs: Grasp the sophisticated processes behind LLM development, translating complex concepts like batch processing, iterative training cycles (epochs), and error correction (loss functions) into digestible culinary steps.
- Crafting “Flavorful” Prompts: Master the art of communication with AI, developing sophisticated prompting strategies like “no-ingredient” (zero-shot), “a few taste tests” (few-shot), and “step-by-step recipe guidance” (chain-of-thought) for optimal results.
- Personalizing Your “Signature Dish”: Learn to tailor LLMs to your unique needs, exploring customization techniques such as fine-tuning and leveraging powerful “culinary tools” like Hugging Face and LoRA.
- Judging the “Taste” of Performance: Develop a discerning palate for evaluating LLM output, utilizing both objective measurements and subjective taste tests to assess model efficacy.
- Serving Up Your AI Creations: Discover practical methods for presenting your LLM-powered applications to the world, from utilizing “delivery APIs” to building “food stalls” with FastAPI/Flask and showcasing them on platforms like Hugging Face Spaces.
- Building End-to-End “Meal Kits”: Construct complete LLM applications from scratch without writing a single line of code, utilizing intuitive no-code platforms and the versatile “recipe book” of LangChain.
- Continuous “Quality Control”: Implement robust systems for ongoing improvement, employing “logbooks,” “customer feedback loops,” and “taste comparison” (A/B testing) to refine your AI models over time.
- PROS:
- Unparalleled Accessibility: Breaks down complex AI concepts into simple, understandable terms for a broad audience.
- Hands-On Project Focus: Emphasizes practical application through a vast array of projects, fostering immediate skill development.
- No-Code Empowerment: Opens the door to AI engineering for individuals without traditional programming backgrounds.
- CONS:
- Depth vs. Breadth Trade-off: While comprehensive, the sheer volume of projects might limit the exploration of highly advanced, specialized LLM topics in extreme detail.
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