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I am a software architect studying AI GPTs like from OpenAI such as ChatGPT 4 but also others and other companies. I am preparing for interviews. It's absolutely vital that I learn all the major points of GPT software so I can do well in the job interview. If I do not do well, I may not be abel to get a job.
Please give me the top 15 categories of info I should know to be prepared for the job interview on AI GPTs:
To excel in a job interview focused on AI and Generative Pre-trained Transformers like OpenAI's GPT models, you should be well-versed in various aspects of machine learning, deep learning, natural language processing, and specifically the transformer architecture that GPT is based on. Here are the top 15 categories of information you should be familiar with:
I am a software architect studying AI GPTs like from OpenAI such as ChatGPT 4 but also others and other companies. I am preparing for interviews. It's absolutely vital that I learn all the major points of GPT software so I can do well in the job interview. If I do not do well, I may not be abel to get a job.
Please give me the top 15 categories of info I should know to be prepared for the job interview on AI GPTs, specifically targeted and ONLY for this topic:
Fundamentals of Machine Learning: Understand supervised, unsupervised, and reinforcement learning paradigms, along with key concepts such as overfitting, underfitting, regularization, and cross-validation.
Deep Learning Concepts: Be able to explain neural networks, backpropagation, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms.
Transformer Architecture: Know the inner workings of the transformer model, including self-attention, positional encoding, multi-head attention, and the significance of these components in parallelizing training.
Language Modeling: Understand what a language model is and how it works, including the concepts of perplexity, tokenization, and sequence prediction.
GPT-specific Architecture: Be familiar with the specific architectural choices of GPT models, such as the use of decoder-only transformers, layer normalization, and modified initialization.
Training and Fine-tuning: Know the process of pre-training on large datasets, followed by fine-tuning on specific tasks, and why this two-stage process is effective.
Datasets and Data Processing: Be aware of the types of datasets GPT models are trained on, data cleaning and preparation, and the ethical considerations of data sourcing.
Evaluation Metrics: Understand how to evaluate language models using metrics such as BLEU, ROUGE, and METEOR for translation and summarization, and more task-specific metrics for other applications.
Scaling and Model Size: Discuss the implications of scaling up models in terms of the number of parameters, computational requirements, and potential benefits and drawbacks.
Applications of GPT: Be ready to talk about practical applications of GPT models, including but not limited to chatbots, content generation, semantic search, code generation, and more.
AI Ethics and Bias: Have a thoughtful perspective on the ethical implications of AI, the potential for bias in large language models, and how companies are addressing these issues.
Knowledge Distillation and Efficiency: Understand techniques for model compression and optimization, such as knowledge distillation, quantization, and pruning, to make models more efficient.
Interpretability and Explainability: Be able to discuss the importance of understanding model decisions and the tools and techniques used to interpret model outputs.
Tooling and Frameworks: Be familiar with the software and frameworks used to develop and deploy GPT models, such as TensorFlow, PyTorch, Hugging Face's Transformers library, and OpenAI's API.
Research and Development Trends: Stay informed about the latest research, trends, and breakthroughs in the field of NLP and transformer models, and be prepared to discuss potential future developments.
In addition to technical knowledge, it's crucial to be able to communicate effectively about these topics, to demonstrate problem-solving skills, and to show how you can apply this knowledge in practical scenarios that you might encounter in the role you're interviewing for.
Fundamentals of Machine Learning: Understand supervised, unsupervised, and reinforcement learning paradigms, along with key concepts such as overfitting, underfitting, regularization, and cross-validation.
Deep Learning Concepts: Be able to explain neural networks, backpropagation, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention mechanisms.
Transformer Architecture: Know the inner workings of the transformer model, including self-attention, positional encoding, multi-head attention, and the significance of these components in parallelizing training.
Language Modeling: Understand what a language model is and how it works, including the concepts of perplexity, tokenization, and sequence prediction.
GPT-specific Architecture: Be familiar with the specific architectural choices of GPT models, such as the use of decoder-only transformers, layer normalization, and modified initialization.
Training and Fine-tuning: Know the process of pre-training on large datasets, followed by fine-tuning on specific tasks, and why this two-stage process is effective.
Datasets and Data Processing: Be aware of the types of datasets GPT models are trained on, data cleaning and preparation, and the ethical considerations of data sourcing.
Evaluation Metrics: Understand how to evaluate language models using metrics such as BLEU, ROUGE, and METEOR for translation and summarization, and more task-specific metrics for other applications.
Scaling and Model Size: Discuss the implications of scaling up models in terms of the number of parameters, computational requirements, and potential benefits and drawbacks.
Applications of GPT: Be ready to talk about practical applications of GPT models, including but not limited to chatbots, content generation, semantic search, code generation, and more.
AI Ethics and Bias: Have a thoughtful perspective on the ethical implications of AI, the potential for bias in large language models, and how companies are addressing these issues.
Knowledge Distillation and Efficiency: Understand techniques for model compression and optimization, such as knowledge distillation, quantization, and pruning, to make models more efficient.
Interpretability and Explainability: Be able to discuss the importance of understanding model decisions and the tools and techniques used to interpret model outputs.
Tooling and Frameworks: Be familiar with the software and frameworks used to develop and deploy GPT models, such as TensorFlow, PyTorch, Hugging Face's Transformers library, and OpenAI's API.
Research and Development Trends: Stay informed about the latest research, trends, and breakthroughs in the field of NLP and transformer models, and be prepared to discuss potential future developments.
In addition to technical knowledge, it's crucial to be able to communicate effectively about these topics, to demonstrate problem-solving skills, and to show how you can apply this knowledge in practical scenarios that you might encounter in the role you're interviewing for.