Kapil Madan's repositories
Diabetic-retinopathy-classification
CNN based Deep learning technique to classify diabetic retinopathy using fundus images of eyes
api-test-framework
API Test Framework using Rest Assured
deep-learning-drizzle
Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
DeepLearning_CNN_Computer-Vision-Video-Analysis_Gesture-Recognition
To recognise five different gestures performed by the user which will help users control the TV without using a remote. The gestures are continuously monitored by the webcam mounted on the TV. Each gesture corresponds to a specific command: Thumbs up: Increase the volume Thumbs down: Decrease the volume Left swipe: 'Jump' backwards 10 seconds Right swipe: 'Jump' forward 10 seconds Stop: Pause the movie. Each video is a sequence of 30 frames (or images). For analysing videos using neural networks, two types of architectures are used commonly. One is the standard CNN + RNN architecture in which you pass the images of a video through a CNN which extracts a feature vector for each image, and then pass the sequence of these feature vectors through an RNN.
kmadan92
🍎Jalpc -- A flexible Jekyll theme, 3 steps to build your website.
linux
Linux kernel source tree
NLP_RASA_Chatbot-for-Restaurant-Search
A restaurant search chatbot using built on RASA. It uses zomato API's to search restaurant in different locations across India. t=The chatbot is able to understand users preference for cuisine and price ranges while searching for restaurants
Reinforcement-Learning_Optimize-Cab-Driving-Strategies
In this highly competitive industry, retention of good cab drivers is a crucial business driver, and you believe that a sound RL-based system for assisting cab drivers can potentially retain and attract new cab drivers. The goal of your project is to build an RL-based algorithm which can help cab drivers maximise their profits by improving their decision-making process on the field.
Statistical-Machine-Learning-Classification-Algo_PCA_Telecom-Churn-Case-Study
In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate. Given the fact that it costs 510 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition. To reduce customer churn, telecom companies need to predict which customers are at high risk of churn. In the Indian and the southeast Asian market, approximately 80% of revenue comes from the top 20% customers (called high-value customers). Thus, if they can reduce churn of the high-value customers, they will be able to reduce significant revenue leakage.
vCOWERT
(v)isually (CO)mpare (WE)bpages in (R)egression (T)esting