sarveshwar22

face-recognition

This python program can recognise face using the famous haar-cascade classifer.

Facebook-Clone

Harry-potter-Invisible-Cloak

Image-Denoising-Using-Autoencoders

Skills learned: Understand the theory and intuition behind Autoencoders, Import Key libraries, dataset and visualize images,Perform image normalization, pre-processing, and add random noise to images, Build an Autoencoder using Keras with Tensorflow 2.0 as a backend ,Compile and fit Autoencoder model to training data ,Assess the performance of trained Autoencoder using various KPIs

Leetcode-Questions

Collection of LeetCode questions to ace the coding interview!

Playground

A python library consisting of pipelines for visual analysis of different sports using Computer Vision and Deep Learning.

TechZen-ECommerceApp

Threejs_Shirt

TICTACTOE-BOT

Tic-Tac-Toe 1.Introduction: Two players, Cross and Nought, take turns to place crosses (X) and noughts (O) in empty cells of a 3X3 array. The player who succeeds in placing three of their marks in a horizontal row, vertical column, or diagonal wins the game. If this doesn't happen, the game ends in a draw. The simplicity of the 3X3 game ensures that best play from both the players ensures a draw. 2.Strategy List of priorities: 1)Win: If the player has two in a row (here, row represents horizontal row or vertical column or diagonal), they can place a third (if empty) to get three in a row. 2)Block: If the opponent has two in a row, the player must play the third themselves to block the opponent's win. 3)Opportunity move: Create an opportunity where the player has two ways to win (two non-blocked lines of 2). It is a move which creates an opportunity for the player to definitely win in the next move. 4)Blocking an opponent's opportunity move (in priority order): Option 1: The player should create two in a row to force the opponent into defending, as long as it doesn't result in them creating an opportunity move. For example, if "X" has two opposite corners and "O" has the center, "O" must not play a corner in order to win. (Playing a corner in this scenario creates an opportunity move for "X" to win.) Option 2: If there is a configuration where the opponent can have an opportunity move, the player should block that move. 5)If none of the above moves exist, the following strategy should be used in the same priority order: i)The player should play a move which can lead to an opportunity move if the opponent doesn't play the next move optimally. ii)The player should play a move which can lead to win in next move, given that the opponent doesn't play optimally. iii)Otherwise any random move can be played. To play optimally, O should adopt the following strategies: i) If X plays corner opening move (best move for them), O should take center in the next move. This will ensure that X has no move to play that will give an opportunity move. Then follow the list of priorities. ii) If X plays edge opening move, O should take center, and then follow the given list of priorities. iii)If X plays center opening move, O should take corner, and then follow the above list of priorities. To play optimally, X should adopt the following strategies: i) X should play the centre as the opening move and then follow the list of priorities.

Time-Series-Analysis-Weather-Data

This is the repository where I store the work done in Time Series Analysis Domain at Bhaskaracharya Institute for Space Applications and Geoinformatics, Gandhinagar

sarveshwar22/Harry-potter-Invisible-Cloak

sarveshwar22/Convolutional-Nueral-Network

sarveshwar22/Cracking-the-Coding-Interview

Learn how to uncover the hints and hidden details in a question, discover how to break down a problem into manageable chunks, develop techniques to unstick yourself when stuck, learn (or re-learn) core computer science concepts, and practice on 189 interview questions and solutions.

sarveshwar22/Cricket-Analytics

Statistical and exploratory Analysis of Cricket Data

sarveshwar22/Flappy-bird-AI

MACHINE LEARNING AI OF GAME FLAPPY BIRD

sarveshwar22/github-demosarvesh

sarveshwar22/Improving-Deep-Nueral-Networks

sarveshwar22/qstp_robot_automation_using_ROS

sarveshwar22/Real-Time-Person-Removal

Removing people from complex backgrounds in real time using TensorFlow.js in the web browser

sarveshwar22/Sequence-Models

sarveshwar22/Simple-Deep-learning-Application-