A Python script to interact with the Scratch API
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Updated
Oct 21, 2021 - Python
A Python script to interact with the Scratch API
Implemented back-propagation algorithm on a neural network from scratch using Tanh and ReLU derivatives and performed experiments for learning purpose
An implementation of the CART algorithm of Decision Tree used for Classification
Nice Homework for Fundamental of Image Processing
C++ code for implementation of Linked lists.
This repository contains a python file that implements 1NN algorithm from scratch in python for the iris and ionosphere dataset
Scratch a variety of semi modal animations
Implementation of logistic regression from scratch using stochastic gradient descent (SGD) algorithm
Rate Limit PoC implemented using Token Bucket Algorith,
Simple blockchain from scratch
Scratch - CS50
Performing K-means clustering on MNIST data from scratch. Instead of using Euclidean distance metric, I have used Cosine Similarity as distance metric. Clustering is done in 10, 7, and 4 clusters for analysis.
This repository contains the implementation (from scratch in python) of a simple neural network to classify MNIST. It can also be used to classify other image datasets.
Implemented KMeans from scratch and trained it on Fashion-MNIST dataset by experimenting with initializaion methods like forgy, random partitions, kmeans++ and found the optimal number of clusters by implementing elbow & silhouette algorithms from scratch
Implemented gradient descent algorithm and its variants from scratch and visualized their results by training models, for comparison and learning purposes
Fraudulent banknote detection with decision trees built from scratch.
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