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Most people want to use Generative AI. Fewer know how to build it. Even fewer know how to build it right. That’s where a roadmap like this becomes essential. I just went through this detailed Generative AI Roadmap, and it lays out a learning path from fundamentals all the way to deploying AI agents and real-world apps. If you're serious about building GenAI skills, here’s what’s included: - Start with core concepts: supervised vs. unsupervised learning, overfitting, basic Python, matrix ops, probability - Move into generative modeling: RNNs, autoencoders, latent space, backprop, VAEs - Deep dive into GANs & diffusion models: StyleGAN, CycleGAN, Stable Diffusion, U-Nets - Explore LLMs for text generation: transformers, attention, prompt engineering, few-shot learning - Go beyond text: music, audio, synthetic data, 3D generation - Learn fine-tuning techniques: LoRA, PEFT, instruction tuning - Then get hands-on with deployment: containerization, quantization, APIs, scaling - And finally, build AI agents with LangChain, CrewAI, and n8n—tying perception, reasoning, and action into workflows This roadmap is perfect for developers, ML engineers, and even product teams looking to understand what it really takes to go from an idea to a working GenAI app. -- Join our Newsletter with 137K Subscribers — www.theravitshow.com