rishab-sharma

chatbot_tensor

[https://rishabbot.herokuapp.com] I implement Tensorflows Sequence to Sequence model to train a chatbot on a reddit/AIML compatible open-source dataset. The bot holds a fun conversation and its just been trained for sometime .

cnn-hand-written-digit

This is the code for a model that recognizes handwritten digit images (MNIST). Developed using TensorFlow and the super simple Keras Library. Wrapped into a Webapp using Flask Micro Framework.

Cross-a-Crater

India has a prominent spot in the space research domain and stands tall along with the developed nations. It has added one more feather to its cap, when on September 26, 2016, India's space agency ISRO launched eight satellites from one rocket into two different orbits. ISRO has been leading the space research since its inception in 1969 and demonstrated its technical prowess by sending “MOM: Mission on Mars” on a minuscule budget. Taking inspiration from these missions, e-Yantra has developed the theme “Cross a Crater” for eYRC-2016. Consider the following scenario: e-Yantra has sent its rover for an expedition to the red planet Mars. The mission is to collect samples to determine if life sustaining factors exist in this planet. While returning to the Base Station from a different route the rover has encountered a huge crater that it needs to cross. The crater has two paths comprising of cavities along the way. These cavities need to be filled using appropriate boulders by taking a feed from the nearest satellite to make them traversable. The above scenario has been simplified and abstracted as an arena for this theme. The arena represents a crater and comprises of two partially traversable bridges, Bridge 1 and Bridge 2 with cavities at random positions leading to the Base Station. The rover takes the feed from a camera directly above it that guides it towards filling the cavities using conical structures and navigating the bridge. Navigating each of the bridges involve different challenges. The rover has to traverse using one of these bridges and reach the Base Station. The teams have to design, program and control an autonomous robot and use image processing techniques to complete the tasks. The team which traverses the bridge and reaches the base in the least possible time will be declared the Winner

emergecy_services

Web Portal For All Type of Emergency Services || They are the Police, Fire Brigade , Ambulance Service and Electricity. Emergency services are usually free. Also a special provision for our geek pals , we also provide a Internet emergency service which you can reach out for help on a attack like DDOS or SQLInjection.

geospatial_map_plotting

MAP PLOTTING USING GEOSPATIAL DATABASE is 2 way project in which a geospatial database can be plotted on a map depicting the various polygons which refer to the lease area of a company along with the point data depicting the wells and boreholes.

image_research

As observed machine learning, computer vision techniques and other computer science algorithms cannot compete the human level of intelligence in pattern recognition such as hand written digits and traffic signs. But here we have reviewed a biologically plausible deep neural network architecture which can make it possible using a fully parameterizable GPU implementation deep neural network independent of the pre-wired feature extractors designing, which are rather learned in a supervised way. In this method tiny fields of winner neurons gives sparsely connected neural layers which leads to huge network depth as found in human like species between retina and visual cortex. The winning neurons are trained on many columns of deep neurons to attain expertise on pre-processed inputs in many different ways after which their predictions are averaged. Also GPU used, enables the models to be trained faster than usual. Upon testing the proposed method over MNIST handwriting data it achieves a near-human performance. Upon considering traffic sign recognition, our architecture has an upper hand by a factor of two. We also tried to improve the state-of-theart on a huge amount of common image classification benchmarks.

nidaan

A webapp for disease recognition and symptoms analysis || Models : CNN , K-Means , Support Vector Classifier

ocr_on_android

An android app to recognise a hand Written Digit using a Flask , Tensorflow Backend , Model Designed using The coolest and simplest Keras

self_driven_future

My most ambitious project for simulating a self driving car with EEG. This is the code for my project where I used Udacity's self driving car simulator as a testbed for training an autonomous car.

snack_search

A machine Learning app for snack suggestion | The goal for this project is to build a system that allows you to identify and then recommend, recipes you're likely to enjoy.

rishab-sharma/chatbot_tensor

[https://rishabbot.herokuapp.com] I implement Tensorflows Sequence to Sequence model to train a chatbot on a reddit/AIML compatible open-source dataset. The bot holds a fun conversation and its just been trained for sometime .

rishab-sharma/ocr_on_android

An android app to recognise a hand Written Digit using a Flask , Tensorflow Backend , Model Designed using The coolest and simplest Keras

rishab-sharma/self_driven_future

My most ambitious project for simulating a self driving car with EEG. This is the code for my project where I used Udacity's self driving car simulator as a testbed for training an autonomous car.

rishab-sharma/nidaan

A webapp for disease recognition and symptoms analysis || Models : CNN , K-Means , Support Vector Classifier

rishab-sharma/snack_search

A machine Learning app for snack suggestion | The goal for this project is to build a system that allows you to identify and then recommend, recipes you're likely to enjoy.

rishab-sharma/Cross-a-Crater

India has a prominent spot in the space research domain and stands tall along with the developed nations. It has added one more feather to its cap, when on September 26, 2016, India's space agency ISRO launched eight satellites from one rocket into two different orbits. ISRO has been leading the space research since its inception in 1969 and demonstrated its technical prowess by sending “MOM: Mission on Mars” on a minuscule budget. Taking inspiration from these missions, e-Yantra has developed the theme “Cross a Crater” for eYRC-2016. Consider the following scenario: e-Yantra has sent its rover for an expedition to the red planet Mars. The mission is to collect samples to determine if life sustaining factors exist in this planet. While returning to the Base Station from a different route the rover has encountered a huge crater that it needs to cross. The crater has two paths comprising of cavities along the way. These cavities need to be filled using appropriate boulders by taking a feed from the nearest satellite to make them traversable. The above scenario has been simplified and abstracted as an arena for this theme. The arena represents a crater and comprises of two partially traversable bridges, Bridge 1 and Bridge 2 with cavities at random positions leading to the Base Station. The rover takes the feed from a camera directly above it that guides it towards filling the cavities using conical structures and navigating the bridge. Navigating each of the bridges involve different challenges. The rover has to traverse using one of these bridges and reach the Base Station. The teams have to design, program and control an autonomous robot and use image processing techniques to complete the tasks. The team which traverses the bridge and reaches the base in the least possible time will be declared the Winner

rishab-sharma/image_to_text

A OCR Python application to detect and convert any text located in an image using OpenCV and Tesseract

rishab-sharma/ml_framework

Common Framework For ML Based Projects

rishab-sharma/neural_turing_machine

A Pytorch Implementation of Neural Turing Machine on various tasks. (Sine Wave Regeneration , Copy Task and Language Translation)

rishab-sharma/Machine-Learning

rishab-sharma/retina

A Deep Learning Driven App for Facial Recognition , Animal Type Classification and Auto Generation of CNN's based upon the user Need

rishab-sharma/rishab

Pypl

rishab-sharma/Youtube-Data-Extraction-and-Analysis

Collect data using Youtube API v3 and analyse using Google Charts.

rishab-sharma/awesome-semantic-segmentation

:metal: awesome-semantic-segmentation

rishab-sharma/darts

Differentiable architecture search for convolutional and recurrent networks

rishab-sharma/docs-personate

rishab-sharma/filecrypt

Fully automated file encryption using OpenSSL

rishab-sharma/hyperas

Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization

rishab-sharma/models

Models and examples built with TensorFlow

rishab-sharma/morphy

Morphy DNN Designing

rishab-sharma/Operating-System

Programs for Operating Systems written in C

rishab-sharma/page

rishab-sharma/Privacy-and-Security-in-Online-Social-Media

rishab-sharma/pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

rishab-sharma/pytorch-CycleGAN-and-pix2pix

Image-to-image translation in PyTorch (e.g., horse2zebra, edges2cats, and more)

rishab-sharma/rishab-sharma

rishab-sharma/Semantic_Human_Matting

Semantic Human Matting

rishab-sharma/serve

Model Serving on PyTorch

rishab-sharma/tSNE-Animation

Hacking sklearn's t-SNE implementation to animate embedding process

rishab-sharma/video-frame-classification