The Generative AI Learning Roadmap Generative AI is a type of AI that can create new content based on what it has learned from existing knowledge. It has the potential to revolutionize human learning. Here’s a GenAI roadmap with learning resources: 1 - Learn about important concepts like Probability, Statistics, Calculus, and Linear Algebra. 2 - Understand the working of foundational models like GPT, MetaAI’s Llama, Gemini, DeepSeek, and Claude. 3 - Learn the GenAI development stack that includes Python, Language, ChatGPT APIs, Prompt Engineering, VectorDB, DeepSeek, Llama, and Huggingface. 4 - Learn how to train and fine-tune a foundation model. 5 - Understand the role of AI Agents and how to build one using GenAI tools. 6 - Learn about GenAI models for computer vision such as GAN (Generative Adversarial Networks), MidJourney, DALL E, Flux, and so on. 7 - Make use of GenAI Learning Resources such as DeepLearning AI platform, Kaggle, Generative AI Insider’s Guide by ByteByteGo, Google Labs, and Nvidia Learning platforms. Over to you: What else will you add to the GenAI learning roadmap? -- Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): bit.ly/bbg-social
@bytebytego our advice: Don't try to master everything before you build. Start small, maybe a personal GPT app or a basic RAG pipeline & let curiosity pull you deeper. The GenAI ecosystem is moving so fast that practical experience quickly becomes your best teacher.
@bytebytego @grok refaz esse roda map traduzindo para português
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@bytebytego Should the `Generative AT` inside the AI circle be `Generative AI`? Seems like a typo
@bytebytego Thanks @bytebytego . Could you also make a list for resources to learn each steps?
