Fundamentals of Generative AI - Ace the AI Engineer Interviews

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Ace the AI Engineer Interviews

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1.Introduction

Course Overview

2.Neural Network Training and Optimization

Neural Networks Training Gradient Descent Transfer Learning Model Alignment Model Compression Fine-Tuning Synthetic Data Generation

3.Embeddings and Tokenization

Embeddings Tokenization Methods Beam Search

4.Attention Mechanisms

Multi-Head Self-Attention Cross-Attention Flash Attention Positional Encodings Masking Normalization in Transformers

5.Evaluation Techniques

Perplexity BLEU and ROUGE

6.Model Architectures and Comparisons

Choosing Between Different Types of AI Models Scaling Laws Elo Rating Systems for LLMs Diffusion Models Model Interpretability Hallucinations and Jailbreaks How ChatGPT Works?

7.Learning Techniques

Few-Shot Learning Chain-of-Thought Prompting RAG for LLMs Choosing Between RAG, ICL, and Fine-Tuning In LLMs

8.Scalability and Efficiency

Mixture of Experts Vector Databases Agentic Errors

9.Wrap Up

Mock Interview

Fundamentals of Generative AI