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Comprehensive introduction to AI and deep learning, covering key concepts, applications, and historical context. Explores data processing, computational requirements, and course structure.
Explore factored cognition through a hands-on livecoding session, diving into Ought's primer to understand complex problem-solving techniques and AI-assisted reasoning.
Explore LLM-based data analysis and synthetic data generation for the "LLM Science Exam" Kaggle competition through a hands-on livecoding session.
Explore LLMOps principles for improving language model applications, covering model selection, prompt management, testing strategies, evaluation metrics, deployment, and test-driven development.
Explore UX principles for Language User Interfaces, including design patterns, case studies of Copilot and Bing Chat, and key considerations for creating effective AI-powered interfaces.
Explore techniques for enhancing language models with external context: retrieval augmentation, chaining, and tool use. Learn about embeddings, databases, and practical applications in AI development.
Learn to rapidly prototype and deploy an AI-powered app using LLMs. Explore the process from initial concept to MVP deployment in just one hour.
Walkthrough of building an LLM-powered Discord bot for answering questions about neural network applications, covering tooling, data cleaning, infrastructure, frontend, embeddings, and monitoring.
Explore foundational concepts of large language models, including machine learning basics, Transformer architecture, and notable LLMs. Gain insights into pretraining datasets and instruction tuning.
Explore intuitions and techniques for effective prompt engineering, including decomposition, reasoning, and reflection, to unlock the full potential of language models.
Explore the future of AI: multimodal robots, scaling limits, AGI possibilities, and safety concerns. Gain insights into key questions shaping the next era of language models and artificial intelligence.
Explore LangChain, a framework for building LLM applications, through a demo and Q&A with its creator. Learn about its features, benefits, and potential applications in AI development.
Explore ethical challenges in ML-powered products, from tech industry crises to AI rights. Learn about fairness, accountability, data ownership, and responsible development in machine learning.
Comprehensive guide to ML team dynamics, hiring strategies, project management, and product design, offering insights for both aspiring professionals and team leaders in the machine learning field.
Explore Transformers, Large Language Models, prompt engineering, and innovative applications like CLIP and image generation in this comprehensive overview of foundation models.
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