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.
Introduction
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:
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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.
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Common applications include text generation (writing articles, stories, code), image generation, audio/speech synthesis, and even generation of videos or 3D objects.
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Popular generative AI models include GPT-4 for text, DALL-E and Stable Diffusion for images, and models like WaveNet for audio generation.
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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.
- Introduction to Generative AI
- Fundamental Concepts
- Generative Models
- Text Generation
- Image Generation
- Audio and Speech Generation
- Video Generation
- Multimodal Generation
- Ethical Considerations and Responsible AI
- Deployment and Production Considerations
- Generative AI Applications and Use Cases
- Future Directions and Emerging Trends
- 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
Future Directions and Emerging Trends
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
Conclusion
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.