Sharpen your skills for AI interviews by diving deep into neural networks, NLP, and transformer models. Master techniques like gradient descent, transfer learning, and model evaluation to stand out.

INT.tar.gz

Main

Pack

This course prepares candidates to confidently tackle AI interviews by covering the most relevant and in-demand topics. You’ll explore neural network training (gradient descent, transfer learning, model compression), language processing (tokenization, embeddings, decoding), and transformer attention mechanisms (self-, cross-attention, and flash attention).

You’ll gain a solid understanding of evaluation metrics like perplexity, BLEU, and ROUGE, and dive into modern AI challenges including hallucinations, jailbreaks, and interpretability. You’ll also learn cutting-edge methods such as RAG, few-shot learning, and Chain-of-Thought prompting—plus explore efficiency, scalability, Mixture of Experts, vector databases, and agentic AI behaviors.

Ace the AI Engineer Interviews

Learn how modern language models use tokenization—especially subword methods like BPE—to convert text into model-ready inputs efficiently and intelligently.

Introduction

Neural Network Training and Optimization

Embeddings and Tokenization

Attention Mechanisms

Evaluation Techniques

Model Architectures and Comparisons

Learning Techniques

Scalability and Efficiency

Wrap Up

Tokenization Methods