Mathematics Behind Large Language Models and Transformers
Deep Dive into Transformer Mathematics: From Tokenization to Multi-Head Attention to Masked Language Modeling & Beyond
4.39 (453 reviews)
2 320
students
4.5 hours
content
Jun 2024
last update
$79.99
regular price
What you will learn
Mathematics Behind Large Language Models
Positional Encodings
Multi Head Attention
Query, Value and Key Matrix
Attention Masks
Masked Language Modeling
Dot Products and Vector Alignments
Nature of Sine and Cosine functions in Positional Encodings
How models like ChatGPT work under the hood
Bidirectional Models
Context aware word representations
Word Embeddings
How dot products work
Matrix multiplication
Programatically Create tokens
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Our Verdict
With its detailed mathematical focus, this course offers valuable insights into transformer internals. Though repetition may irk some learners and prerequisites assume basic linear algebra knowledge, the 'Mathematics Behind Large Language Models and Transformers' prepares AI professionals for deeper understanding of seminal paper 'Attention is all you need'. Be prepared to study theory without coding examples.
What We Liked
- 'Math Behind Large Language Models and Transformers' dives deep into mathematical concepts, like tokenization to multi-head attention.
- Clear explanations of complex algorithms give learners a solid foundation in transformer architectures.
- Engaging insights on positional encodings, bidirectional language models, vectors, and dot products are well presented.
- Comprehensive content, published in 2024, resonates with research work of AI engineers and researchers.
Potential Drawbacks
- Repetition has been a common theme among learners, with some finding it helpful while others consider it repetitive.
- Expectations management: this course heavily emphasizes theory; coding practices aren't covered—software development skills are not an explicit focus.
- The pace and prerequisite knowledge of linear algebra might present a challenge for absolute beginners, making parts of the course demanding.
- A more engaging training section would benefit learners, as it appears underdeveloped compared to the rich theoretical content.
6029496
udemy ID
18/06/2024
course created date
15/07/2024
course indexed date
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