Generative AI (Gen AI)

Discover a comprehensive roadmap to mastering generative AI. This learning path covers the foundational concepts, core generative models, different modalities of generation, ethical considerations, deployment aspects, and future trends.

Generative AI Roadmap


Generative AI refers to artificial intelligence systems that are capable of generating new data, such as text, images, audio, or other forms of content, rather than simply analyzing or classifying existing data.

Here are some key points about generative AI:

  • It uses machine learning models, especially deep learning, to learn patterns from training data and then generate brand new outputs that mimic the patterns in the data.

  • Common applications include text generation (writing articles, stories, code), image generation, audio/speech synthesis, and even generation of videos or 3D objects.

  • Popular generative AI models include GPT-4 for text, DALL-E and Stable Diffusion for images, and models like WaveNet for audio generation.

  • The models learn to capture the statistical patterns, semantics, and context in the training data extremely well, allowing high-quality and coherent generation.

Generative AI is considered a breakthrough that enables AI systems to produce original content rather than simply sorting/classifying existing data. However, there are concerns around bias, copyright issues, deepfakes, and the potential misuse of these powerful generative capabilities.

Generative AI (Gen AI) Learning Path

This roadmap covers the foundational concepts, core generative models, different modalities of generation (text, image, audio, video, and multimodal), ethical considerations, deployment aspects, future trends, and real-world applications of Generative AI. It provides a comprehensive overview of the field, starting from the basics and progressing towards advanced topics and emerging trends.

  1. Introduction to Generative AI
  2. Fundamental Concepts
  3. Generative Models
  4. Text Generation
  5. Image Generation
  6. Audio and Speech Generation
  7. Video Generation
  8. Multimodal Generation
  9. Ethical Considerations and Responsible AI
  10. Deployment and Production Considerations
  11. Generative AI Applications and Use Cases
  12. Future Directions and Emerging Trends
  13. Resources and Further Learning

Introduction to Generative AI

Discover the fundamentals of generative AI, its applications, use cases, challenges, and limitations in this comprehensive introduction.

  • What is Generative AI?
  • Applications and Use Cases
  • Challenges and Limitations

Fundamental Concepts

Explore the essential concepts underpinning generative AI, including machine learning, deep learning, neural networks, and probabilistic models.

  • Machine Learning Basics
  • Deep Learning and Neural Networks
  • Probabilistic Models and Bayesian Inference

Generative Models

Learn about various generative models like VAEs, GANs, autoregressive models, flow-based models, and diffusion models that power generative AI.

  • Variational Autoencoders (VAEs)
  • Generative Adversarial Networks (GANs)
  • Autoregressive Models
  • Flow-based Models
  • Diffusion Models

Text Generation

Dive into language models, transformer architecture, attention mechanisms, transfer learning, and evaluation metrics for text generation tasks.

  • Language Models
  • Transformer Architecture
  • Attention Mechanisms
  • Transfer Learning and Fine-tuning
  • Evaluation Metrics and Challenges

Image Generation

Understand CNNs, GAN architectures, diffusion models for image generation, image-to-image translation, and evaluation metrics in this insightful section.

  • Convolutional Neural Networks (CNNs)
  • GAN Architectures for Image Generation
  • Diffusion Models for Image Generation
  • Image-to-Image Translation
  • Evaluation Metrics and Challenges

Audio and Speech Generation

Gain insights into speech synthesis, text-to-speech models, voice conversion, music and audio generation, and their evaluation metrics.

  • Speech Synthesis
  • Text-to-Speech (TTS) Models
  • Voice Conversion
  • Music and Audio Generation
  • Evaluation Metrics and Challenges

Video Generation

Explore video prediction, video-to-video translation, generative adversarial video generation, motion transfer, and evaluation metrics for video generation.

  • Video Prediction
  • Video-to-Video Translation
  • Generative Adversarial Video Generation
  • Motion Transfer
  • Evaluation Metrics and Challenges

Multimodal Generation

Discover vision-language models, text-to-image generation, image captioning, visual question answering, and evaluation metrics for multimodal tasks.

  • Vision-Language Models
  • Text-to-Image Generation
  • Image Captioning
  • Visual Question Answering
  • Evaluation Metrics and Challenges

Ethical Considerations and Responsible AI

Ensure ethical and responsible AI by addressing bias, fairness, privacy, security, transparency, interpretability, societal impact, and governance.

  • Bias and Fairness
  • Privacy and Security
  • Transparency and Interpretability
  • Societal Impact and Governance

Deployment and Production Considerations

Learn practical aspects like model optimization, scalability, distributed training, model serving, inference, monitoring, and maintenance.

  • Model Optimization and Acceleration
  • Scalability and Distributed Training
  • Model Serving and Inference
  • Monitoring and Maintenance

Generative AI Applications and Use Cases

Explore real-world use cases and applications of generative AI in natural language generation, image/video synthesis, generative art/design, and healthcare/biotechnology.

  • Natural Language Generation
  • Image and Video Synthesis
  • Generative Art and Design
  • Generative Music and Audio
  • Generative AI in Healthcare and Biotechnology

Stay ahead with insights into self-supervised learning, unsupervised generative models, cross-modal generation, generative AI for creativity, art, and scientific discovery.

  • Self-Supervised Learning
  • Unsupervised Generative Models
  • Multimodal and Cross-Modal Generation
  • Generative AI for Creativity and Art
  • Generative AI for Scientific Discovery

Resources and Further Learning

Find valuable resources for learning Gen AI - online courses, books, research papers, communities, conferences, tools, and other relevant artifacts.

  • Online Courses and Tutorials
  • Books and Research Papers
  • Online Communities and Forums
  • Gen AI Conferences and Events
  • Gen AI Development Tools and Frameworks
  • Gen AI Ethics and Policy Resources


We hope you find our Generative AI (Gen AI) learning path useful.

Discover everything you need to know about building for the emerging web by following these structured learning paths at your own pace.

This roadmap was last updated on: 02:52:07 20 May 2024 UTC

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