Technology on Pranav Buradkar https://pranavburadkar.github.io/categories/technology/ Recent content in Technology on Pranav Buradkar Hugo -- 0.164.0 en-us Mon, 27 Jul 2026 15:00:00 +0530 Demystifying Mixed Precision Training: Speeding Up Deep Learning with FP16 and BF16 https://pranavburadkar.github.io/posts/mixed-precision-training/ Mon, 27 Jul 2026 15:00:00 +0530 https://pranavburadkar.github.io/posts/mixed-precision-training/ An in-depth guide on how mixed precision training works, the mathematics of loss scaling, the difference between FP16 and BF16, and how to implement it in PyTorch. Mastering NVIDIA Omniverse Kit SDK: From App Templates to Custom Extensions https://pranavburadkar.github.io/posts/omniverse-kit-sdk/ Sun, 14 Jun 2026 11:00:00 +0530 https://pranavburadkar.github.io/posts/omniverse-kit-sdk/ An in-depth developer guide to building modular 3D applications, microservices, and custom UI extensions using the NVIDIA Omniverse Kit SDK. A Simple Guide to LLM Serving: Quantization, KV Caching, and Continuous Batching https://pranavburadkar.github.io/posts/understanding-llm-optimization/ Sun, 10 May 2026 10:00:00 +0530 https://pranavburadkar.github.io/posts/understanding-llm-optimization/ Dive into the three key techniques that make serving large language models (LLMs) fast, cheap, and memory-efficient. How to Fine-Tune Llama 3 on Your Own Data: A Practical Guide https://pranavburadkar.github.io/posts/fine-tune-llama-3-lora/ Sun, 12 Apr 2026 10:00:00 +0530 https://pranavburadkar.github.io/posts/fine-tune-llama-3-lora/ Step-by-step practical guide to fine-tuning Meta's Llama 3 using PyTorch, Hugging Face Transformers, and PEFT/LoRA on a custom dataset. A Simple Guide to LoRA (Low-Rank Adaptation) https://pranavburadkar.github.io/posts/understanding-lora-fine-tuning/ Fri, 20 Mar 2026 10:00:00 +0530 https://pranavburadkar.github.io/posts/understanding-lora-fine-tuning/ Learn how Low-Rank Adaptation (LoRA) makes fine-tuning massive LLMs and generative models incredibly efficient. Deep Learning Fundamentals: Attention Mechanisms and Vision Transformers https://pranavburadkar.github.io/posts/deep-learning-fundamentals-attention-vit/ Mon, 15 Dec 2025 10:00:00 +0000 https://pranavburadkar.github.io/posts/deep-learning-fundamentals-attention-vit/ Breaking down the mechanics of Self-Attention and understanding how the Transformer architecture leaped from Natural Language Processing to dominating Computer Vision with ViTs. A Simple Guide to Variational Autoencoders (VAEs) https://pranavburadkar.github.io/posts/simple-guide-to-vae/ Sun, 30 Nov 2025 10:00:00 +0530 https://pranavburadkar.github.io/posts/simple-guide-to-vae/ Dive into the world of VAEs, understand how they generate new data, and explore their applications in AI. A Beginner's Guide to LLM Quantization: GGUF, GPTQ, and AWQ https://pranavburadkar.github.io/posts/beginners-guide-to-llm-quantization/ Thu, 20 Nov 2025 10:00:00 +0000 https://pranavburadkar.github.io/posts/beginners-guide-to-llm-quantization/ Expanding on LLM optimization by explaining how massive models are compressed using techniques like GGUF, GPTQ, and AWQ to run on consumer hardware. A Simple Guide to GANs https://pranavburadkar.github.io/posts/simple-guide-to-gans/ Thu, 21 Aug 2025 08:52:47 +0530 https://pranavburadkar.github.io/posts/simple-guide-to-gans/ Understand the core concepts of GANs, how they work, and their applications in AI.