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AI Engineering Course

1h 36m 46s
English
Paid

Course description

This course is designed to help programmers and developers transition into the field of artificial intelligence engineering. You will thoroughly explore vector databases, indexing, large language models (LLM), and the attention mechanism.
Read more about the course

By the end of the course, you will understand how LLMs work and be able to use them to create real applications.

What you will learn:

  • Develop mental models of how LLMs in the style of GPT work
  • Understand processes such as tokenization, embeddings, attention, and masking
  • Optimize LLM inference using caching, batching, and quantization
  • Design and deploy RAG pipelines using vector databases
  • Compare methods: prompt engineering, fine-tuning, and agent-based architectures
  • Debug, monitor, and scale LLM systems in production

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#1: Course Intro

All Course Lessons (21)

#Lesson TitleDurationAccess
1
Course Intro Demo
02:01
2
Usecase
01:48
3
How are vectors constructed
06:43
4
Choosing the right DB
03:27
5
Vector compression
03:27
6
Vector Search
06:59
7
Milvus DB
05:38
8
LLM Intro
00:43
9
How LLMs work
08:31
10
LLM text generation
03:08
11
LLM improvements
05:10
12
Attention
05:28
13
Transformer Architecture
03:40
14
KV Cache
08:28
15
Paged Attention
04:38
16
Mixture Of Experts
04:01
17
Flash Attention
03:40
18
Quantization
03:33
19
Sparse Attention
05:14
20
SLM and Distillation
05:31
21
Speculative Decoding
04:58

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Books

Read Book AI Engineering Course

#Title
11. Vector+Embeddings+&+Semantic+Space
22. Compression+&+Quantization_+Scaling+Vectors+Efficiently-4
33. Indexing+Techniques_+Making+Vector+Search+Scale
44. Search+Execution+Flow_+From+Query+to+Result
55. LLMs+and+RAG
66. What+is+Attention+and+Why+Does+It+Matter
77. Paged+Attention
88. Quantization+Summary

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