Generative Al NLP Specialization | Level 1

Introducing the Generative AI NLP Specialization Level 1 Event!

Dive into the dynamic realm of Generative AI NLP (Natural Language Processing) in our exclusive event. Unleash the potential of generative models and techniques that breathe life into text and embeddings.

Whether you're an accomplished AI aficionado or just stepping into the world of NLP, this event caters to all skill levels. Embark on a journey to understand the core concepts and methodologies of Generative AI NLP.

Key Takeaways:

• Grasp the theoretical underpinnings of generative models
• Gain hands-on experience through interactive labs
• Explore real-world applications of Generative AI
• Implement generative models in your own projects

Who Should Attend:

• Developers and Software Engineers curious about NLP with Generative AI
• AI Enthusiasts and Professionals building intelligent and innovative solutions
• Data Scientists and ML Practitioners eager to enhance their GenAI skills

Prerequisites

Prior experience with our Foundation for Generative AI course or equivalent knowledge is recommended.

Join us to get started on this exciting journey to understanding Generative AI and NLP. Seize the opportunity to broaden your skill set and amplify your career prospects. Secure your spot today and be at the forefront of AI innovation!

Limited slots available. Reserve yours now for an event that promises to shape the future of NLP with Generative AI.

Topics Covered

Module 1: Getting started with Generative AI

• Understanding Fundamentals of Generative AI
• Types of Generative Models - autoregressive models, variational autoencoders, and generative adversarial networks (GANs)
• GAN Architecture and tuning process
• GAN variants
• Generating synthetic data using GANs
• Categorizing generative models based on learning algorithms: likelihood-based vs. likelihood-free
• Motivation for generative modeling compared to discriminative models
• Characteristics of generative models: density estimation, data simulation, representation learning

Module 2: Text Generation

• Techniques of generating text using generative models
• Text generation using RNNs & Transformers.
• Industry Use Cases
• Lab: RNN & Transformer based Text Generation

Module 3: Generating Embeddings

• Generating embeddings
• Embedding techniques
• Sentence/Document embeddings
• Lab: Embeddings

Module 4: Vector Databases -

• Introduction to Vector Database
• Building a Vector database
• Lab: Building a Vector DB