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audio_emotion_recognition

Emotion Recognition from audio using Ravdess Dataset

becoss.github.io

covid-19-analysis

Covid transmission in countries compared along side the countries' GDP and happiness index

fashion-mnist

gan-mri

Code repository for Frontiers article 'Generative Adversarial Networks for Image-to-Image Translation on Multi-Contrast MR Images - A Comparison of CycleGAN and UNIT'

gesture-recognition

You want to develop a cool feature in the smart-TV that can recognise five different gestures performed by the user which will help users control the TV without using a remote.

heart-attack-analysis

Heart Attack Analysis Prediction based on patient history - Kaggle

lead-conversion-analysis

Lead conversion Analysis to increase conversion rate for an online education platform

low-light-object-detection-

nluproject

Hate/Offensive Text Detection in Hindi using Word Embeddings

phonological-loop-

The Phonological Loop is an auditory component in the working memory that is dedicated to the retention of verbal information. The loop is an adversarial system in which refresh and decay of the word occurs simultaneously and the two contrasting process struggle between remembrance and forgetfulness of the words.

skin-cancer-detection

superagi-assignment

SuperAGI placement Assignment

telecom-customers-retention-analysis-

Business problem overview 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 5-10 times more to acquire a new customer than to retain an existing one, customer retention has now become even more important than customer acquisition. For many incumbent operators, retaining high profitable customers is the number one business goal. To reduce customer churn, telecom companies need to predict which customers are at high risk of churn. In this project, you will analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.

torch_saliency_metrics

traffic-sign-classification-using-tensorflow-keras

The German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011. We cordially invite researchers from relevant fields to participate: The competition is designed to allow for participation without special domain knowledge. Our benchmark has the following properties: Single-image, multi-class classification problem More than 40 classes More than 50,000 images in total Large, lifelike database