utkarsh-bajpai Goto Github PK
Name: Utkarsh Bajpai
Type: User
Location: Zurich, Switzerland
Name: Utkarsh Bajpai
Type: User
Location: Zurich, Switzerland
Compared Bayesian, elastic net, random forest and standard regression using grid search for brain age prediction. Removed outliers, highly correlated features. Used lasso to find relevant features.
Detecting roads in satellite imagery using deep learning
Repository for assignments completed during the Coursera course deeplearning.ai
TensorFlow Implementation of "Unsupervised Cross-Domain Image Generation"
Google Stock Prediction Using Linear Regression with an accuracy of 98% and saving the trained classifier using Pickle. It also uses multi-threading to reduce the training time.
A Handwritten Digit Recognition System to evaluate the effectiveness of various types of classifiers such as MLP classifier, K Nearest Neighbour Classifier, SVC, Decision Tree Classifier, Random Forest Classifier, AdaBoost Classifier and GaussianNB in recognising handwritten digits.
This is a static app which displays a Birthday Card on the screen.
This is a Coffee Ordering app which shows the final amount the user has to pay.
Deep Learning for humans
Implementing Mathematics of Linear Regression from Scratch
This repository contains our improvements of the algorithm introduced in [1]. Following the course curriculum our efforts concentrated on restructuring, unrolling and vectorizing various elements of the computations. Furthermore we have experimented with different data structures and memory layouts.
Name generation and classification using recurrent neural network
DeepZ is a method for local robustness verification, based on zonotopes, a type of convex relaxations, and abstract transformations. While affine transformations and convolutions can be represented exactly using this framework, a sound over-approximation has to be used to approximate the ReLU function. The abstract transformer used in DeepZ is parameterized by one parameter per hidden unit. In this work we propose an optimization strategy for this parameterization to increase the maximum verifiable image perturbation for both fully-connected and convolutional network architectures.
R-NET implementation in Keras.
Repo for code of Practical Machine Learning Tutorial with Python Youtube tutorial by sentdex. The objective of this course is to give a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms.
Software Engineering Project-App for booking a mode of trasport.
TensorFlow Tutorial and Examples for Beginners with Latest APIs
Examples built with TensorFlow.js
Exercises for my tutorials on Theano
Tensorflow Implementation of Deeper LSTM+ normalized CNN for Visual Question Answering
A complete timetable management system for Faculties, Students or any VIT club/chapter.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.