AI Engineering Course

1h 36m 46s
English
Paid
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.
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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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# Title Duration
1 Course Intro 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

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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