Overview
Curriculum
In this module, you will explore key concepts of the transformer architecture, embeddings, attention mechanisms, and tokenization. You’ll gain a deeper understanding of semantic similarity and how it is calculated using techniques like dot product and cosine similarity. The module also includes hands-on exercises to help you apply the concepts learned to real-world scenarios.
What You'll Learn
- Understand the fundamentals of the transformer architecture and how it is used in modern LLMs.
- Analyze the role of embeddings, attention, and self-attention mechanisms in processing and generating text.
- Learn tokenization techniques and their importance in preparing text data for transformer models.
- Evaluate methods for calculating semantic similarity, such as dot product and cosine similarity, in transformer models.
Courses you might be interested in
In this module, we’ll build the foundational programming knowledge and theory needed to succeed in the bootcamp. By covering essential Python concepts and tools, we’ll set ourselves up for a...
-
11 Lessons
$100.00
In this module, we’ll focus on data exploration, visualization, and feature engineering—essential steps in preparing data for analysis. We’ll learn how to use techniques like summary statistics and visual tools...
-
14 Lessons
$100.00
In this module, we’ll explore how to turn raw data into compelling visual stories. We’ll learn how to choose the right visualizations for different data types, and apply tools like...
-
12 Lessons
$100.00
In this module, we’ll explore how to use predictive modelling to create real business impact. We’ll learn to identify the right opportunities for machine learning, translate business goals into actionable...
-
8 Lessons
$100.00