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aind--20_facial-keypoints-detection

Udacity's Artificial Intelligence Nanodegree - Project #20

convolutionalchef

Designed a deep learning based python notebook application for identification of ingredients from images and recommendation of food recipes based on these identified ingredients. The training of model was achieved using ResNet-50 and ResNet-101 model. The user may provide the ingredients in text format instead of images.

coursera_helper_chatbot

A chatbot to help coursera student with their difficulty.

cvnd--01_facial-keypoints

Udacity's Computer Vision Nanodegree - Project #01

depression-prediction-using-machine-learning

dermatologist-ai

detecting-depression-in-tweets-using-machine-learning-classifiers

Machine learning, when used with social media, can be useful in diagnosis of mental health illnesses as it provides insights into an individual’s behaviour. In this study, we compared several machine learning classifiers on their ability to detect if a given tweet that talks about depression actually shows signs of depression. We fetched tweets from Twitter using a hashtag keyword matching procedure and used a pre-trained BERT sentiment model to separate potential tweets about depression into two classes. Since prior research indicated that including emojis can improve classification performance when working with social media textual data, we used Word2Vec and Emoji2Vec to create embeddings for both text and emojis in the tweets. Our best performing model was a Gaussian kernel support vector machine (SVM) with a test accuracy of around 85% both with and without emojis. Contrary to our expectations, including emojis did not noticeably improve performance which we attribute primarily to our limited dataset.

development-of-depression-detection-model-with-ensemble-classifiers-and-multi-layer-perceptron.

In this era of digital connectivity and consequent increasing human disconnection, depression numbers are rising at an alarming rate. According to World Health Organization, globally, more than 300 million people of all ages suffer from depression and at it's worst depression can lead to suicide. In order to detect such disorder, machine learning techniques could conceivably provide exceptional attributes that facilitate in scrutinizing the unique patterns that remain unseen and process them to determine the psychological state of social media users. The designed detector takes text as an input from the user and returns the likelihood of depressive sentiment. Internally, the system utilizes multilayer perceptron model weights for making the prediction.

facial-keypoint-detection

Facial keypoint detection system takes in any image with faces, and predicts the location of 68 distinguishing keypoints on the face - Udacity project

gordon-ramsai

Self-supervised Seq2Seq neural network able to generate new step-by-step recipes based on the name of the dish the user wants to make and the ingredients the user would like to include in it

hand_detection

image-classification

This Machine learning Image classification uses scikit-learn SVM image classification algorithm

keras

Deep Learning for humans

p1_facial_keypoints

First project for CVND: facial keypoint detection.