4.5
Intermediate
10h
Updated 4 months ago
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.
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.
This course prepares candidates to confidently tackle AI interviews by covering the most relevant and in-demand topics. You’ll e...Show More
WHAT YOU'LL LEARN
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
An understanding of strategies for training, optimizing, and fine-tuning neural networks and generative AI models
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TAKEAWAY SKILLS
Content
2.
Neural Network Training and Optimization
7 Lessons
Review the fundamental aspects and techniques behind training models efficiently, from optimization parameters to advanced training strategies.
3.
Embeddings and Tokenization
3 Lessons
Explore embeddings, tokenization, and beam search for effective AI text generation.
4.
Attention Mechanisms
6 Lessons
Explore key attention mechanisms, normalization techniques, and evaluation metrics in transformer models.
5.
Evaluation Techniques
2 Lessons
Master key metrics for evaluating language models, including perplexity, BLEU, and ROUGE.
6.
Model Architectures and Comparisons
7 Lessons
Explore AI model selection, scaling laws, evaluation methods, and challenges in generative AI.
7.
Learning Techniques
4 Lessons
Master techniques to enhance large language models for effective AI/ML applications.
8.
Scalability and Efficiency
3 Lessons
Explore advanced AI concepts like Mixture of Experts, vector databases, and agentic errors.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
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