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PinnedJAIGANESANinTowards AIKAN (Kolmogorov-Arnold Networks): A Starter Guide 🐣Exploring MLP, the Universal Approximation theorem, the Kolmogorov-Arnold Representation theorem, the Bezier Curve, B-splines, and KAN.Jul 4Jul 4
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PinnedJAIGANESANExploring LLM: A Collection of My Articles❤️Dive into the intricate world of large language models with in-depth articles on their architectures, MoE, and RAG. Discover more by…Jun 25Jun 25
JAIGANESANinTowards AISAM 2 (Segment Anything Model 2) is Amazing, But We Need to understand SAM 1.Explained Segment Anything Model 1 with Architecture, and CodeAug 13Aug 13
JAIGANESANinAI AdvancesUnderstand RNN from Four Perspectives: Architecture, Math, Code, and Visual WorkflowA must read article to understand RNNJul 31Jul 31
JAIGANESANinTowards AIMulti-task Learning (MTL) and The Role of Activation Functions in Neural Networks [Train MLP With…Two concepts in Deep Learning- Simple and Important.Jul 221Jul 221
JAIGANESANinTowards AIQuantization: Post Training Quantization, Quantization Error, and Quantization Aware TrainingEfficient Inference in AI ModelsJul 17Jul 17
JAIGANESANinTowards AIA Visual Walkthrough of DeepSeek’s Multi-Head Latent Attention (MLA) 🧟♂️Exploring Bottleneck in GPU Utilization and Multi-head Latent Attention Implementation in DeepSeekV2Jun 20Jun 20
JAIGANESANinTowards AIRetrieval Augmented Generation (RAG): A Comprehensive Visual Walkthrough 🧠📖🔗🤖A Simple Illustrated GuideJun 192Jun 192