Kush Blogs
Hello there! 👋
This is my personal blog where I document my learning journey. Feel free to explore the tags or the featured posts below.
List of Blogs
★ Featured
Inference Engine
Building an infernece engine for machine translation transformer model in C++.
6. NLP: Transformers
A complete guide to the transformer architecture, attention mechanisms, and positional encoding.
5. NLP: Attention
A guide on Attention mechanism for seq2seq models.
Latest Writings
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3. Maths4ML: Kernel Trick
The Kernel Trick - Folding Space. Why struggle to bend the line when you can just fold the space?#Maths for ML
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3. Maths4ML: Dot Product
Shadows, Angles, and the Geometry of Sameness#Maths for ML
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2. Maths4ML: Distances & Norms
Measuring the gap - Rulers of ML#Maths for ML
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1. Maths4ML: Vectors, Basis & Spans
The Building Blocks – Vectors, Basis & Spans#Maths for ML
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11. NLP: Tokenizers
A detailed description about tokenizers in LLMs.#NLP
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10. NLP: Mixture of Experts (MOE)
A guide on Mixture of Experts with their history and implementation in transformers along with the losses associated with it.#NLP
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9. NLP: Optimizing Attention
A guide on KV-Caching along with Sliding Window Attention. Ending with MQA and GQA.#NLP
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8. NLP: BERT
A guide on BERT models and how their architecture.#NLP
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7. NLP: Transformer Implementation
Implementation and trainig of Transformer for En-Hi machine translation task.#NLP #Transformers
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4. NLP: Seq2Seq
A guide on Seq2Seq models that changed machine translation task.#NLP
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3. NLP: Pytorch for NLP
Some tips for working with PyTorch for NLP tasks#NLP
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2. NLP: Long Short Term Memory [LSTM] & Gated Recurrent Unit [GRU]
A guide on LSTM and GRU models and how they overcome limitations of RNNs.#NLP
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1. NLP: Recurrent Neural Networks [RNN]
A guide on the first steps towards modern NLP.#NLP
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8. Computer Vision: Neural Style Transfer
Mini Project performing style transfer from a source image to a target image.#Computer Vision
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7. Computer Vision: Instance Segmentation
A small description on Instance Segmentation.#Computer Vision
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6. Computer Vision: Object Detection
A detailed description on Object Detection models.#Computer Vision
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5. Computer Vision: Semantic Segmentation
A small guide on semantic segmentation of images.#Computer Vision
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4. Computer Vision: Fine Tuning Models
A small guide on how to fine-tune pre-trained models.#Computer Vision
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3. Computer Vision: Transfer Learning
A small guide on how transfer learning using pre-trained models.#Computer Vision
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2. Computer Vision: Pre trained Models
A small guide on how to work with pre-trained models.#Computer Vision
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1. Computer Vision: Architecture
A description on working of CNNs.#Computer Vision
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6. Neural Nets: Regularization and Normalization
Regularization Techniques.#Neural-Net
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5. Neural Nets: Implementing MLP using PyTorch
Implementing Multi Layer Perceptron#Neural-Net
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3. Neural Nets: Activation Functions
Overview of differnet types of Actiavation functions.#Neural-Net
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4. Neural Nets: Loss and Optmization
Dicussion about different types of Optimization functions and losses#Neural-Net
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2. Neural Nets: Preprocessing data
Brief description on how data can be processed.#Neural-Net
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1. Neural Nets: Overview
Overview of Neural Networks#Neural-Net