facebookresearch/dlrm — An implementation of a deep learning recommen...
🥀 A recommendation engine that uses supervised learning techniques to predict user’s future trips on the basis of their past trips and preferences. Uses Microsoft Azure to implement machine learning techniques to create a user-specific profile. The recommendation system is built on deep learning (neural networks with backpropagation). The neural network was trained to recommend house listings which coincided with the user’s traits. It is a Spring MVC application using MongoDB and Morphia. 1.3 million listings and user data were stored in MongoDB. The listings that were recommended to the users were fed into MongoDB and were displayed in the application using Google Maps API. Being a frequent Airbnb user myself, my wish for a more personalized experience for planning my future Airbnb trips inspired the concept behind this project.
🍂 Deep Learning has emerged as a new area in machine learning and is applied to a number of image applications. The main purpose of the this work is to apply the concept of a Deep Learning algorithm namely, Convolutional neural networks (CNN) in image classification. The algorithm is tested on standard COCO datasets. The performance of the algorithm is evaluated based on the quality metric known as Mean Squared Error (MSE) and classification accuracy. The experimental result analysis based on the quality metrics and the graphical representation proves that the algorithm (CNN) gives fairly good classification accuracy for all the tested datasets. Then we used visualization technique on the particular image for understanding which part of a given image led to convert to its final classification decision. For this we used CAM Visualisation technique. We also tried in doing object detection by using Pytorch.
1 ⭐ (0)LeadingIndiaAI/-DESIGN-AND-IMPLEMENTATION-OF-PROCESSING-MODULE-FOR-OBJECT-DETECTION-AND-WEAPON-CLASSIFICATION-WITH-
🍂 BiDAF: Question Answering models in NLP is an intriguing field but there are many challenges before we a model can reach human level comprehension. This is my first attempt at exploring how to implement a Reading Comprehension model using Pytorch deep learning framework.
1 ⭐ (0)aparajita15/BiDAF-implementation