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.

Learn about Generative Adversarial Networks, Natural Language Processing, Deep Learning, Neural Networks, and Large Language Models. Discover key concepts, applications, and techniques in AI and machine learning.