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Henry Harvin® offers a tailor-made latest Generative AI Course for all enthusiasts | Learn Core Generative Models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformers and Large Language Models (LLMs) | Don't miss a chance to learn Applications of Generative AI in your field to create original content
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The Generative AI course allows for identifying patterns and structures in existing data, generative AI is a family of artificial intelligence systems that may produce new material, including text, photos, music, and videos. Generative AI is intended to create completely new and unique outputs, in contrast to standard AI models, which are mostly focused on classification or regression tasks (i.e., finding patterns or generating predictions).
It is a versatile 10-in-1 program that includes various aspects of competency development and career development.
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Definition, history, and real-world impact
Types: GANs, VAEs, Transformers, Diffusion Models.
Comparisons and use cases
Bias, fairness, and ethical AI principles.
Case studies on responsible AI.
Setup: Python, TensorFlow, PyTorch, and Hugging Face.
Tour of available pre-trained generative model
Data collection and preprocessing.
Model training, evaluation, and deployment overview
Concept: Latent space and reconstruction.
Hands-on: Build a simple VAE for generating digit images (MNIST)
Fundamentals: Generator-Discriminator interaction.
Advanced GAN types: Conditional GANs and StyleGAN.
Hands-on: Train a GAN for custom image generation
Transformer architecture: Self-attention mechanism.
Case study: GPT (overview and working).
Hands-on: Fine-tune a transformer for text summarization or Q&A
AI for content creation: Summarization, translation, and chatbot design.
Hands-on: Fine-tune an LLM for text completion or dialogue generation
Techniques: Image synthesis, editing, and style transfer.
Hands-on: Create unique images using GANs and diffusion models (e.g., Stable Diffusion).
Audio generation: Speech synthesis and music creation.
Hands-on: Generate synthetic voices using pre-trained models
Brainstorming and implementing a use case in healthcare, marketing, or gaming.
Group discussions on industry-specific challenges
Techniques: Model optimization, containerization, and API creation.
Hands-on: Deploy a generative model with Flask or FastAPI
Multimodal models like CLIP and GPT-4 Vision.
Advances in efficiency (e.g., LoRA, quantization, distributed training)
Participants work on a guided mini-project based on their chosen domain.
Present and receive feedback on their projects
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Model Optimization
Containerization
API Creation
GPT-4 Vision
GANs
MNIST
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