Gaurav Sen
@gkcs_ • 71,305 subscribers
CEO of InterviewReady
Videos
Engineers need to communicate effectively when building AI Systems. These terms will help you use a shared vocabulary. This is useful when discussing concepts, reading papers, or collaborating with teammates. Listed in order. 00:00 Agenda 00:28 1. Large Language Model 01:28 2. Tokenization 02:53 3. Vectorization 04:15 4. Attention 07:22 5. Self-Supervised Learning 12:07 6. Transformer 14:32 7. Fine-tuning 17:05 8. Few-shot Prompting 18:11 9. Retrieval Augmented Generation 20:33 10. Vector Database 23:03 11. Model Context Protocol 25:43 12. Context Engineering 28:17 13. Agents 29:19 14. Reinforcement Learning 34:42 15. Chain of Thought 35:55 16. Reasoning Models 36:36 17. Multi-modal Models 38:21 18. Small Language Models 40:24 19. Distillation 41:47 20. Quantization If you are a software engineer looking to transition to AI, click the link below. AI Engineering Course: #AI #SoftwareEngineering #Agents
Gaurav Sen79,241 次观看 • 8 个月前
This video is a response to the recent IT rules. These rules have been suggested by the Ministry of Electronics and Information Technology. They raise serious concerns on free speech in India. Thanks to Internet Freedom Foundation (IFF) for flagging this. I have shared a template that we can use to connect with Meity regarding this. Link: Please raise your voice, while you still can. Cheers :)
Gaurav Sen20,761 次观看 • 1 个月前
This video explains how diffusion models are overtaking Large Language Models for generation tasks like: 1. Code Generation 2. Image Generation 3. Video Generation 00:00 Agenda 00:20 How are they different from LLMs? 05:09 Internal Mechanism 10:09 How are vectors generated? 12:08 Conclusion 13:02 Opinion Piece AI Engineering Course: #Diffusion #AI #LLMs
Gaurav Sen27,910 次观看 • 7 个月前
Amazon S3 stores exabytes of data across millions of disks. This video explains it's internal architecture. In this video, we break down: 1. How S3 uploads files using parallel multipart uploads 2. How checksums and monitoring prevent data corruption 3. How S3 survives disk, shard, and even region failures 4. How shuffle sharding and request cancellation keep latency low 5. How auto-scaling avoids hot prefixes 6. How error-correcting codes deliver 11 nines of durability with only ~1.8× storage overhead If you want to understand real-world distributed storage design, this is how S3 does it at scale. Links: #AWS #S3 #SystemDesign
Gaurav Sen22,111 次观看 • 5 个月前
In this video, we break down the Model Context Protocol — a massively underrated concept in AI that's quietly redefining what large language models can do. While most people are distracted by the hype, MCP is solving a core problem: LLMs that can perform actions. We dive into: 1. The limitations of current AI systems 2. What makes MCP different 3. How MCP can reduce engineering overhead 4. Real-world scenarios where MCP shines 00:00 What is MCP? 01:48 Usecase - SEO 03:50 Usecase - RAG 04:50 Usecase - Apps 06:30 Conclusion 07:00 The future? #MCP #AI #LLM
Gaurav Sen41,097 次观看 • 1 年前
This is how large language models turn objects to vector representations. In this video, we explore how large language models (LLMs) convert objects into internal representations, especially when translating between languages like English and Hindi. Using real-world examples, we highlight the challenges of gender inference, grammatical structure, and why direct word-to-word translations often fail. If you're curious about how LLMs deal with multilingual contexts and what it takes to improve translation quality across languages, this video is for you. #LLMs #Vectors #LCM
Gaurav Sen26,776 次观看 • 1 年前