Core concepts of generative AI involve neural networks, adversarial training (GANs), variational inference (VAEs), and the ability of models to learn and generate data that resembles the training data distribution.
- Explore the fundamentals of generative AI, including its evolution, key architectures, and language representations.
- Analyze text preprocessing methods such as tokenization, stemming, and lemmatization to prepare data for generative AI models.
- Examine foundational concepts of natural language processing (NLP) and its progression to advanced models that enable language generation.
- Evaluate the mechanics of neural networks and their role in building context for generative AI applications.
- Investigate the encoder-decoder framework for sequence-to-sequence tasks and its impact on modern language models.
- Understand the principles of prompt engineering to effectively communicate with generative AI models.
Apply foundational concepts to create innovative generative AI applications across text, image, and audio domains.
Implement model optimization techniques to enhance the efficiency and accuracy of generative AI systems in real-world settings.
Assess generative AI models using intrinsic and extrinsic metrics to ensure quality and reliability in applications.
Guide teams in developing and deploying generative AI solutions, addressing ethical considerations and emerging trends.
Learning Roadmap
1.
Introduction to Generative AI
Introduction to Generative AI
2.
Building Blocks of Generative AI
Building Blocks of Generative AI
3.
Foundation Models
Foundation Models
11 Lessons
11 Lessons
4.
Intelligent Interaction with GenAI
Intelligent Interaction with GenAI
3 Lessons
3 Lessons
5.
Practical Applications and Case Studies
Practical Applications and Case Studies
5 Lessons
5 Lessons
6.
Future of Generative AI and Wrap Up
Future of Generative AI and Wrap Up
2 Lessons
2 Lessons
Khayyam Hashmi
Computer scientist and Generative AI and Machine Learning specialist. VP of Technical Content @ educative.io.
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