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Beginner's Guide to Generative AI

These tutorials will provide you with a solid foundation in Generative AI and prepare you for your career goals.

  • Introduction to Generative AI
  • History and Evolution of Generative AI 
  • Applications of Generative AI 
  • Generative AI vs Traditional AI 
  • Popular Generative AI Models
  • Ethical Considerations in Generative AI 
  • Generative AI in Various Industries
  • Generative Adversarial Networks (GANs) 
  • What are GANs?
  • Architecture of GANs: Generator and Discriminator
  • Variants of GANs: DCGAN, CycleGAN, StyleGAN
  • Variational Autoencoders (VAEs)
  • Understanding VAEs 
  • Applications of VAEs 
  • Transformers
  • Overview of Transformer Models 
  • Introduction to GPT and BERT 
  • Applications in Text Generation
  • Deep Learning Basics for Generative AI 
  • Neural Networks Overview
  • Introduction to Natural Language Processing (NLP)
  • Computer Vision for Image and Video Generation 
  • Reinforcement Learning and Its Role
  • Dataset Collection for Generative Models 
  • Data Cleaning and Preprocessing 
  • Labeling and Annotation Techniques 
  • Working with Large Datasets
  • Loss Functions in Generative AI 
  • Model Training Basics
  • Overfitting and Underfitting in Generative Models 
  • Hyperparameter Tuning
  • Using Pre-trained Models
  • Transfer Learning for Generative AI
  • Setting Up the Development Environment 
  • Introduction to PyTorch for Generative AI 
  • TensorFlow Basics for Generative AI
  • Building a Simple GAN 
  • Implementing a VAE
  • Fine-Tuning a Transformer Model 
  • Evaluation Metrics for Generative Models
  • Text Generation
  • Writing Articles and Stories 
  • Chatbot Development
  • Image and Video Generation 
  • Style Transfer
  • Creating Deepfakes
  • Audio and Music 
  • Generation Composing Music
  • Speech Synthesis 
  • Synthetic Data Generation
  • Drug Discovery and Medical Applications
  • Multimodal Generative AI
  • Large Language Models (LLMs) 
  • GPT Series Overview
  • Using OpenAI Models 
  • Explainability in Generative AI
  • Handling Bias in Generative AI Systems 
  • Generative AI for Personalization
  • Hugging Face for NLP 
  • OpenCV for Computer Vision
  • Generative AI APIs: OpenAI, Google AI 
  • Frameworks: PyTorch Lightning, Keras 
  • Model Deployment with TensorFlow Serving 
  • Cloud Platforms for Generative AI
  • Building a Text Generator
  • Developing a Style Transfer Application 
  • Creating a Deepfake Video
  • Synthesizing Music Using Generative Models 
  • Designing a Personalized Chatbot 
  • Generating Synthetic Data for Training
  • Understanding Latent Spaces
  • Random Sampling Techniques
  • Gradient Descent in Model Optimization
  • Matrix Operations in AI Models
  • Probability in Generative Processes
  • Addressing Bias in Generative AI 
  • Combating Misinformation
  • Regulatory Frameworks for Generative AI 
  • Privacy Concerns and Data Security
  • Evaluating Model Performance 
  • Metrics: BLEU, FID, and Perplexity 
  • Optimizing Models for Scalability 
  • Deploying Generative AI Models
  • Real-time AI Image Generation App
  • Multi-language Chatbot for Customer Support 
  • AI-Powered Creative Writing Tool

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