Generative AI and Deep Learning

Generative AI

Generative Adversarial Networks (GANs)

• Consist of two neural networks: generator and discriminator
• Generator creates new samples, discriminator evaluates the generated samples
• Goal: generator produces samples that are indistinguishable from real data
• Applications: image generation, data augmentation, style transfer

Natural Language Processing (NLP)

• Subfield of AI that deals with human language understanding and generation
• Tasks: language translation, sentiment analysis, text summarization
• Techniques: recurrent neural networks (RNNs), long short-term memory (LSTM) networks, transformers

Deep Learning

• Subset of machine learning that uses neural networks with multiple layers
• Enables learning of complex patterns in data
• Applications: image recognition, speech recognition, natural language processing

Neural Networks

• Modeled after human brain's neural structure
• Consist of layers of interconnected nodes (neurons)
• Types: feedforward, recurrent, convolutional

Large Language Models

• Trained on massive amounts of text data (e.g. billions of parameters)
• Examples: transformer-based models like BERT, RoBERTa, and XLNet
• Capabilities: language understanding, text generation, question answering, language translation
• Applications: chatbots, virtual assistants, language translation systems

Note: These study notes provide a concise overview of the topics, focusing on key concepts and ideas. They are meant to serve as a starting point for further exploration and learning.