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Charles Frye on Full Stack Deep Learning - Weaviate Podcast #57!
Hey everyone! Thank you so much for watching the 57th Weaviate podcast with Charles Frye! Charles is an educator at Full Stack Deep Learning, one of the world's top courses on Deep Learning with lectures available on YouTube (link below)! This was one of the most thorough Weaviate podcasts published so far, covering all sorts of topics around the evolution of Deep Learning! Particularly we discussed the Retrieval-Augmented Generation stack with Vector Databases and Zero-Shot Large Language Models and how that compares to more conventional machine learning workflows and the MLOPs stack! I really enjoyed chatting with Charles and am more than happy to answer any questions or discuss any ideas you have about the content in the podcast! Thank you so much for listening!
Check out Full Stack Deep Learning! https://fullstackdeeplearning.com/
Full Stack Deep Learning on YouTube! https://www.youtube.com/@The_Full_Stack
Chapters
0:00 Welcome Charles Frye!
0:52 Charles’ journey into Deep Learning
3:00 Weights & Biases and MLOps
5:30 Retrieval-Augmented Generation Stack
8:58 Data Engines and AI Products
13:50 Fine-Tuning
16:35 Information Retrieval Techniques
20:10 RAG as Tool Use and RETRO
23:33 Gorilla and Fine-Tuned Tool Use
27:36 Text-to-SQL Tool Use
30:46 Generative Data Augmentation
33:05 LLM generated queries for embeddings
38:04 Long-Tail and Data Imbalance
41:45 LoRA LLM Fine-Tuning
44:50 Eigenvectors and Disentaglement
50:00 LLM for Each User
55:00 Embedding Visualization and ML Observability
58:40 GPU Utilization
1:05:05 Discord Q&A Bot App
1:16:10 Data Schema Design
1:21:25 Graph and Vector Databases
1:28:35 Future Directions in AI
View all episodes
By Weaviate
4
44 ratings
Hey everyone! Thank you so much for watching the 57th Weaviate podcast with Charles Frye! Charles is an educator at Full Stack Deep Learning, one of the world's top courses on Deep Learning with lectures available on YouTube (link below)! This was one of the most thorough Weaviate podcasts published so far, covering all sorts of topics around the evolution of Deep Learning! Particularly we discussed the Retrieval-Augmented Generation stack with Vector Databases and Zero-Shot Large Language Models and how that compares to more conventional machine learning workflows and the MLOPs stack! I really enjoyed chatting with Charles and am more than happy to answer any questions or discuss any ideas you have about the content in the podcast! Thank you so much for listening!
Check out Full Stack Deep Learning! https://fullstackdeeplearning.com/
Full Stack Deep Learning on YouTube! https://www.youtube.com/@The_Full_Stack
Chapters
0:00 Welcome Charles Frye!
0:52 Charles’ journey into Deep Learning
3:00 Weights & Biases and MLOps
5:30 Retrieval-Augmented Generation Stack
8:58 Data Engines and AI Products
13:50 Fine-Tuning
16:35 Information Retrieval Techniques
20:10 RAG as Tool Use and RETRO
23:33 Gorilla and Fine-Tuned Tool Use
27:36 Text-to-SQL Tool Use
30:46 Generative Data Augmentation
33:05 LLM generated queries for embeddings
38:04 Long-Tail and Data Imbalance
41:45 LoRA LLM Fine-Tuning
44:50 Eigenvectors and Disentaglement
50:00 LLM for Each User
55:00 Embedding Visualization and ML Observability
58:40 GPU Utilization
1:05:05 Discord Q&A Bot App
1:16:10 Data Schema Design
1:21:25 Graph and Vector Databases
1:28:35 Future Directions in AI
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