Chapter 14 – Introduction to Recent Advancements in Machine Learning

1. Transformer-based text generation, VAEs, and GANs.
2. Different versions of LLaMA and GPT.
3. The generator, which could be a neural network architecture for generating desired data types, such as images, generates images aiming to fool the discriminator into recognizing the generated data as real data. The discriminator learns to remain good at recognizing generated data compared to real data.
4. You can improve your prompting by being specific about the question and specifying for whom the data is being generated.
5. In RLHF, the reward is calculated based on the feedback of humans, either experts or non-experts, depending on the problem. But the reward is not like a predefined mathematical formula considering the complexity of problems such as language modeling. The feedback provided by humans results in improving the model step by step.
6. The idea of contrastive learning is to learn representations...