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.
Intermediate
34 Lessons
10h
Certificate of Completion
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.
AI-POWERED
AI-POWERED
This course includes
This course includes
Course Overview
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, j...Show More
TAKEAWAY SKILLS
Generative Ai
Transformer Models
Large Language Models (llms)
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
What You'll Learn
An understanding of strategies for training, optimizing, and fine-tuning neural networks and generative AI models
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Course Content
Introduction
Neural Network Training and Optimization
Embeddings and Tokenization
Attention Mechanisms
Evaluation Techniques
Model Architectures and Comparisons
7 Lessons
Learning Techniques
4 Lessons
Scalability and Efficiency
3 Lessons
Wrap Up
1 Lesson
Fundamentals of Generative AI
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