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 |
|---|---|
| 1 | 1. Vector+Embeddings+&+Semantic+Space |
| 2 | 2. Compression+&+Quantization_+Scaling+Vectors+Efficiently-4 |
| 3 | 3. Indexing+Techniques_+Making+Vector+Search+Scale |
| 4 | 4. Search+Execution+Flow_+From+Query+to+Result |
| 5 | 5. LLMs+and+RAG |
| 6 | 6. What+is+Attention+and+Why+Does+It+Matter |
| 7 | 7. Paged+Attention |
| 8 | 8. Quantization+Summary |
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