Technical Fridays | Blog
Personal notes on machine learning, deep learning, and software engineering — what's Technical Fridays?
- Personal
- Data Science
- Machine Learning
- R
- Python
- Algorithms
- Cryptography
- Mathematics
- Visualization
- Deep Learning
- Computer Vision
- Natural Language Processing
- Generative AI
- Speech Recognition
- PyTorch
- LLM
- CUDA
- Agentic AI
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Agentic AIIntroduction to Model Context Protocol (MCP)
MCP is an open-source protocol that standardizes how LLMs connect to external tools and data sources, replacing fragile custom integrations with a common interface.
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LLMDistributed Training: How to train Large Language Models (LLM)
Comprehensive guide to distributed training for LLMs covering data parallelism, model parallelism, tensor parallelism, ZeRO optimizer, FSDP, and DeepSpeed with code examples.
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LLMVision Language Models (VLM)
Overview of Vision Language Models (VLMs) and their training paradigms: contrastive learning (CLIP), masking (FLAVA), generative approaches (CoCa, Chameleon), and pretrained backbone methods (Frozen, LLaVA, BLIP-2).
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CUDAMatrix Multiplication in CUDA
Implementing matrix multiplication in CUDA from a naive CPU baseline to GPU-accelerated versions using tiled shared memory for deep learning workloads.
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LLMRetrieval Augmented Generation (RAG) Chatbot for 10Q Financial Reports
Building a RAG-based chatbot for 10Q financial reports to reduce LLM hallucinations by grounding answers in retrieved document context.
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Computer VisionPyTorch Basic Tutorial
A practical introduction to PyTorch covering tensors, autograd, neural network modules, and key libraries like torchvision and torchaudio.
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Computer VisionColor and color spaces in Computer Vision
Understanding color models (RGB, HSV, LAB) and color spaces in computer vision, how computers represent and work with color.
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Deep LearningIntroduction to Panoptic Segmentation: A Tutorial
Panoptic segmentation unifies semantic and instance segmentation assigning class labels and unique IDs to every pixel in an image.
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Deep LearningEvaluation metrics for object detection and segmentation: mAP
How IoU, precision-recall curves, and mean Average Precision (mAP) are used to evaluate object detection and segmentation models.
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Deep LearningQuick intro to Instance segmentation: Mask R-CNN
Instance segmentation with Mask R-CNN: combining object detection and semantic segmentation to identify and segment each object instance separately.