Generative AI and Deep Learning

Study Notes

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.

Description

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.