01
Shift before exponentiating
Subtract the maximum logit to stay stable when confidence gets extreme.
gradientfield
Neural networks / 04
The gradient beneath confidence
Softmax
backward
A compact implementation of the derivative that turns a classifier's confident mistake into a useful learning signal.
∂L / ∂zᵢ = softmax(z)ᵢ − yᵢ
cross-entropy loss · logits z · target y
cross-entropy loss · logits z · target y
softmax_backward.py
import numpy as npdef softmax(logits): shifted = logits - logits.max(axis=-1, keepdims=True) exp = np.exp(shifted) # numerical stability return exp / exp.sum(axis=-1, keepdims=True)def softmax_cross_entropy_backward(logits, target): probs = softmax(logits) grad = probs.copy() grad[np.arange(len(target)), target] -= 1.0 return grad / len(target) # dL / d(logits)logits = np.array([[2.4, 0.8, -0.2]])gradient = softmax_cross_entropy_backward(logits, [0])
02
Probability minus truth
The elegant derivative: predicted distribution, adjusted at the target index.
03
One signal per logit
Positive gradients lower excess confidence; the negative target gradient raises it.
built for learning in motion
Available for internships & early-career roles
Gurugram, India · B.Tech CS ’27
I build systems that learn from data — then ship them.
I'm Krishna Sharma, a Computer Science student and ML practitioner. I turn raw datasets into useful models and working products — from CNNs on MNIST to production APIs.
01 — About
About me
Gurugram · B.Tech CS · 2027I'm a fourth-year B.Tech Computer Science student at J.S. University, Shikohabad, graduating in 2027. My work sits between applied machine learning and software engineering: I like models that get deployed, not notebooks that get closed.
As a Software Development Intern at WritED Edutech, I built a React frontend and improved technical SEO for a JavaScript-rendered single-page app. Competitive programming keeps my foundations in algorithms, graphs, and dynamic programming sharp.
Right now I'm deepening ML theory while building small, complete, end-to-end projects — each one shippable, each one documented.
02 — Projects
four projects
Selected work
2025—2026four projects
Handwritten Digit Classifier
A convolutional neural network trained on MNIST, built from data loading to an inference pipeline with synthetic test cases and a structured README.
CNNmodel
MNISTdata
Sentiment Analysis System
A reusable text-classification module using TF-IDF and scikit-learn to predict sentiment polarity, packaged with executed results instead of placeholder output.
TF-IDFfeatures
sklearnmodel
URL Shortener API
A production-style backend service for shortening and redirecting URLs, built with FastAPI and PostgreSQL and containerized with Docker.
APIbackend
writed.in — Frontend & SEO
Built WritED Edutech's production React frontend, then audited technical SEO for a hash-routed SPA to improve crawlability and search visibility.
Reactfrontend
03 — Toolkit
Toolkit
what I work with dailyMachine learning
- Neural networks & backprop core
- CNNs / image classification applied
- TF-IDF / classical NLP applied
- TensorFlow & scikit-learn tools
Software engineering
- Python daily
- C++ daily
- React.js applied
- FastAPI, PostgreSQL, Docker applied
Algorithms & math
- DP, graphs, trees competitive
- Number theory competitive
- Calculus & optimization applied
- LeetCode & Codeforces ongoing
04 — Experience
Experience
2025 — Present · Gurugram / Remote
WritED Edutech Private Limited
Software Development Intern
- Built the writed.in frontend from scratch in React.js — from design to production deploy.
- Led technical SEO auditing and optimization for a JavaScript-rendered SPA (hash routing → crawlable, indexable).
- Designed a daily content pipeline for RBI Grade B exam-prep material.
2023 — Present · Self-directed
Independent Study & Competitive Programming
Self-directed · Kaggle · Codeforces
- Regularly solve problems across DP, graph theory, and number theory.
- Built and published end-to-end ML projects on Kaggle with clean, reproducible notebooks.