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Ace the AI Engineer Interviews
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.
4.6
34 Lessons
10h
Updated 2 months ago
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- An understanding of strategies for training, optimizing, and fine-tuning neural networks and generative AI models
- Familiarity with tokenization, embeddings, and decoding techniques used in language models and frequently tested in AI interviews
- An understanding of attention mechanisms and architectural innovations that power transformer models
- Familiarity with tools and metrics to evaluate generative model performance and output quality
- Comparative knowledge of AI model architectures, scaling laws, and interpretability methods
- An understanding of advanced techniques for prompting, retrieval-augmented generation (RAG), and few-shot learning
- Familiarity with key concepts in making generative models more efficient, scalable, and robust in production
Learning Roadmap
2.
Neural Network Training and Optimization
Neural Network Training and Optimization
Review the fundamental aspects and techniques behind training models efficiently, from optimization parameters to advanced training strategies.
3.
Embeddings and Tokenization
Embeddings and Tokenization
3 Lessons
3 Lessons
Explore embeddings, tokenization, and beam search for effective AI text generation.
4.
Attention Mechanisms
Attention Mechanisms
6 Lessons
6 Lessons
Explore key attention mechanisms, normalization techniques, and evaluation metrics in transformer models.
5.
Evaluation Techniques
Evaluation Techniques
2 Lessons
2 Lessons
Master key metrics for evaluating language models, including perplexity, BLEU, and ROUGE.
6.
Model Architectures and Comparisons
Model Architectures and Comparisons
7 Lessons
7 Lessons
Explore AI model selection, scaling laws, evaluation methods, and challenges in generative AI.
7.
Learning Techniques
Learning Techniques
4 Lessons
4 Lessons
Master techniques to enhance large language models for effective AI/ML applications.
8.
Scalability and Efficiency
Scalability and Efficiency
3 Lessons
3 Lessons
Explore advanced AI concepts like Mixture of Experts, vector databases, and agentic errors.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
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, and model compression), language processing (tokenization, embeddings, and decoding), and transformer attention mechanisms (self-attention, 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. You’ll explore efficiency, scalability, Mixture of Experts, vector databases, and agentic AI behaviors.
Trusted by 2.9 million developers working at companies
A
Anthony Walker
@_webarchitect_
E
Evan Dunbar
ML Engineer
S
Software Developer
Carlos Matias La Borde
S
Souvik Kundu
Front-end Developer
V
Vinay Krishnaiah
Software Developer
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