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When preparing for a job interview focused on AI GPTs (Generative Pre-trained Transformers) like OpenAI's ChatGPT, it's important to have a solid understanding of the underlying deep learning concepts. Here are the top 15 categories of information you should be familiar with:

1. Neural Networks (NNs)​

  • Understand the basic structure of NNs, including neurons, weights, biases, and activation functions.
  • Be able to explain how NNs can model complex functions through their layers.

2. Backpropagation​

  • Grasp the concept of backpropagation and its role in training neural networks by adjusting weights to minimize loss.
  • Know the chain rule of calculus as it's used to compute gradients efficiently.

3. Convolutional Neural Networks (CNNs)​

  • Explain the architecture of CNNs and their use in processing grid-like data, such as images.
  • Understand convolutional layers, pooling layers, and fully connected layers.

4. Recurrent Neural Networks (RNNs)​

  • Describe how RNNs can process sequences of data by maintaining a hidden state that captures temporal dependencies.
  • Discuss the challenges of training RNNs, such as vanishing and exploding gradients.

5. Attention Mechanisms​

  • Explain the attention mechanism and how it allows models to focus on specific parts of the input sequence, improving the performance of tasks like machine translation.
  • Understand the difference between self-attention and guided attention.

6. Transformer Architecture​

  • Describe the transformer model architecture and its reliance on self-attention mechanisms.
  • Discuss how transformers achieve parallelization and handle long-range dependencies.

7. Pre-training and Fine-tuning​

  • Understand the concepts of pre-training on large datasets and fine-tuning on specific tasks.
  • Explain how GPT models leverage unsupervised learning during pre-training.

8. Language Modeling​

  • Be able to define language modeling and its importance in generating coherent text.
  • Discuss how GPT models use language modeling as a pre-training objective.

9. Tokenization and Embeddings​

  • Explain the process of tokenization and how text is converted into numerical form that can be processed by neural networks.
  • Understand the role of embeddings in capturing semantic meaning.

10. Loss Functions​

  • Discuss common loss functions used in training deep learning models, such as cross-entropy loss.
  • Explain how the choice of loss function impacts the training process and model performance.

11. Optimization Algorithms​

  • Understand optimization algorithms like Stochastic Gradient Descent (SGD), Adam, and RMSprop.
  • Discuss the role of learning rate and other hyperparameters in the optimization process.

12. Regularization Techniques​

  • Explain techniques like dropout, L1/L2 regularization, and data augmentation, and their role in preventing overfitting.
  • Discuss the importance of regularization in model generalization.

13. Sequence-to-Sequence Models​

  • Describe seq2seq models and their applications in tasks like translation and summarization.
  • Understand how encoder-decoder architectures work.

14. Evaluation Metrics​

  • Be familiar with metrics used to evaluate the performance of language models, such as perplexity, BLEU score, and ROUGE.
  • Discuss the importance of aligning evaluation metrics with task-specific goals.

15. Ethical Considerations and Bias​

  • Discuss the ethical implications of language models, including potential biases and the generation of harmful content.
  • Understand strategies to mitigate bias and ensure ethical use of AI models.

In preparation for your interview, you should be comfortable discussing each of these topics in detail, providing examples of how they relate to GPT models, and potentially discussing any recent advancements or research in these areas.