Deep Learning

Our Solution Smart Rural Ecosystem aims to dissolve the unwanted interventions of middle-men in their daily livelihood, which depends on day to day heavy tasks they perform and could not get profit they actually deserve. Using appropriate tech stack and dependencies, we would try to propose an ecosystem, which if adopted, could be result in getting achievements and benefit the people living in rural areas.Project repository for course 18CSC206J - Software Engineering and Project Management [SEPM]

Generally, we try to find out the best suitable models, loss functions, and optimizers, which could help to obtain better accuracy by minimum loss. Since we have to deal with textual data, we might also consider it as a sequence problem, with or without contextual relations exploration, which might add a few problems and load while training our model. The main question that remains unanswered — How to select the best model or algorithm for multi-class classification in NLP? Let’s try to explore and answer that.

When it comes to applying deep learning principles to natural language processing, contextual information weighs in a lot! In the past few years, it has been shown that various improvement in existing neural network architectures concerned with NLP has shown an amazing performance in extracting featured information from textual data and performing various operations for a day to day life. One of the models which we will be discussing in this article is encoder-decoder architecture along with the attention model. The encoder-decoder architecture for recurrent neural networks is actually proving to be powerful for sequence-to-sequence-based prediction problems in the field of natural language processing such as neural machine translation and image caption generation. We will try to discuss the drawbacks of the existing encoder-decoder model and try to develop a small version of the encoder-decoder with an attention model to understand why it signifies so much for modern-day NLP applications!

Conjexure is a machine learning web app for forecasting the stock prices of certain companies into the future. Conjexure utilizes the stock prices for Alphabet Inc. (GOOGL) and Apple Inc. (AAPL) for training. This is primarily because the closing stock price graph for these companies over time is quite smooth overall and avoids any sudden ups or downs and follows a healthy upwards trend.

Focused to develop a smart AI Based digital Platform Using Machine Learning /AI and Web based skills to make a secure web application , which aims to analyze the user’s information,it can be a Faculty or Student. Using Appropriate Tech Stack , this app tries to analyse and solve the problem of online assignment upload, to check plagiarism of the assignments sent by the students, the teacher can check if any student had copied the assignments or not through our ML model, to give a short summary and finding the main key points in the assignment so that it could save time for the teacher and student counselling where student can predict a career or predict dropout rate using our ml model..

Built using Natural Language Processing and Machine Learning,Emergency Ally is a Comprehensive Web App which can help you provide SOS or Emergency Level Solutions , In Case You encounter or see someone in any Danger or Disaster Situtations.

Detailed Comparison of Hybrid Deep Neural Models and Optimizers for Multiclass classification in NLP. Multi-class classification based on textual data refers to a supervised machine learning task, where we try to predict a certain class for input data, given the input data in raw textual format. Well, thanks to TensorFlow, we have a variety of algorithms available to perform multi-class classification via natural language processing. Here , I have tried to perform a detailed analysis and comparison of various hybrid combination of deep neural networks and choosing different optimizer’s impact on their learning and training of model and hence , their performance and time taken to train.

Devising a semi supervised learning approach for determining topic from corpus.

A Basic Conversational Chatbot Made with COVID-19 DATA and Integrated with COVID-19 Tracker’s API to get real time details of Cases from all across India.

Developed using NLP and CV, Our web application aims to analyze the user’s mental health using a common therapy technique–journaling. The user will submit diary entries into our application. In the therapist-patient scenario, they will be required to keep their camera on.