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adversarialnetspapers

The classical paper list with code about generative adversarial nets

agricultural-price-prediction-and-visualization-on-android-app

In Agriculture Price Monitioring , I have used data provided by open government site data.gov.in, which updates prices of market daily . Working Interface Details: We have provided user choice to see current market prices based on two choices: market wise or commodity wise use increase assesibility options. Market wise: User have to provide State,District and Market name and then select market wise button. Then user will be shown the prices of all the commodities present in the market in graphical format, so that he can analyse the rates on one scale. This feature is mostly helpful for a regular buyer to decide the choice of commodity to buy. He is also given feature to download the data in a tabular format(csv) for accurate analysis. Commodity Wise: User have to provide State,District and Commodity name and then select Commodity wise button. Then user will be shown the prices of all the markets present in the region with the commodity in graphical format, so that he can analyse the cheapest commodity rate. This feature is mostly helpful for wholesale buyers. He is also given feature to download the data in a tabular format(csv) for accurate analysis. On the first activity user is also given forecasting choice. It can be used to forecast the wholesale prices of various commodities at some later year. Regression techniques on timeseries data is used to predict future prices. Select the type of item and click link for future predictions. There are 3 java files Forecasts, DisplayGraphs, DisplayGraphs2 ..... Please change the localhost "server_name" at time of testing as the server name changes each time a new server is made. Things Used: We have used pandas , numpy , scikit learn , seaborn and matplotlib libraries for the same . The dataset is thoroughly analysed using different function available in pandas in my .iPynb file . Not just in-built functions are used but also many user made functions are made to make the working smooth . Various graphs like pointplot , heat-map , barplot , kdeplot , distplot, pairplot , stripplot , jointplot, regplot , etc are made and also deployed on the android app as well . To integrate the android app and machine learning analysis outputs , we have used Flask to host our laptop as the server . We have a separate file for the Flask as server.py . Where all the the necessary stuff of clint request and server response have been dealt with . We have used npm package ngrok for tunneling purpose and hosting . A different .iPynb file is used for the time series predictions using regression algorithms and would send the csv file of prediction along with the graph to the andoid app when given a request .

awesome-activity-prediction

Paper list of activity prediction and related area

awesome-deep-learning-papers

The most cited deep learning papers

awesome-public-datasets

A topic-centric list of high-quality open datasets in public domains. New PR ☛☛☛

basic-ml-algos-from-scratch

KNearestNeighbour , Logistic Regression , Linear Regression , Naive Bayes , K Means Clustering

batman-blog

Django based blog with full database handling of user and all utility function a user would need. (registration , login (along with facebook and google) , create , read , update , delete (CRUD) , profile , subscription , email sending (webmailer), uploading files and cropping , rendering pdfs, managing passwords , searching, making queries with database present , exclusive permissions to some users of interest)

cats-vs-dogs-cnn-using-keras-

The training set consisted of 25,000 images out of which 5,000 images were taken out as validation data. Separate test data folder consisted of 12,500 images for which the labels were predicted using trained model. My work includes preprocessing for model, Data augmentation to prevent overfitting, callbacks in keras to reduce learning rate timely, various CNN architecture trials with different layers and hyperparameters for best fit and learning curve wrt epochs. I gained a validation accuracy of 87.15 % without using any pretrained imagenet models . VGG-16 gave around 89 % as validation accuracy.

clr

coursera-applied-data-science-with-python

Repository for coursera specialization Applied Data Science with Python by University of Michigan

coursera-ml

Machine learning assignments submitted while taking Andrew Ng machine learning course at coursera .

cryptocurrencyprediction

Predict Cryptocurrency Price with Deep Learning

deep-learning-coursera

Deep learning assignments submitted while taking Andrew Ng Deep learning course at coursera .

deep-learning-papers-reading-roadmap

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

devol

Genetic ConvNet architecture search with Keras

driver-drowsiness-detection

Driver Drowsiness Detection using Open CV , python , Jupyter Notebooks . The project as a system detects your eyes every time using a webcam and gives a Alert message (can be in form of alarm also) when a set threshold is reached .

face-emotion-classification-for-dementia-patients

The product being developed is a mobile application for android operating system. It is an emotion and pain assessment tool and can be incorporated on other platforms also, which satisfy the minimum requirements of system. The application will allow the doctors to select or capture an image of the patient to be assessed. Then the image will be uploaded to the server and given to the Convolutional Neural Network model to process. The model is trained to generate score of each possible emotion. Then the severity algorithm will work on generated scores. The result will be sent to app.

ganhacks

starter from "How to Train a GAN?" at NIPS2016

gender-recognition-by-voice-0.97004-accuracy-

The voices of different people are tested for 20 properties. These properties include mean-frequency, standard deviation, kurtosis, skew, mode frequency , modulation index , fundamental freq....,etc . My work includes the demonstation of the much probable properties showcased by females as well as males. The study of important attributes for voice recognition and their varied concentration in each gender using the inferences drawn from the various regression plots, pair plots, scatter plots , etc. Dataset is also standardized or normalized prior to training for better performance. Different models are tried . Also plotted their accuracy curves to understand the variation of parameters wrt accuracy. The parameters were tuned using repetitive piecewise gridsearch to compute things efficiently wrt time. Support Vector Machines are taken much care off till end and gave a cross-validated accuracy of 97.004 %. Further a train test spilt accuracy of 99.36 % given by XGBoost Classifier.

house-prices-predictions-with-81-features

The dataset consists of 81 features of each house(lot prop. , garage prop. , basement prop. , year built, .....) which almost contains each and every minute detail of each house along with their respective sale prices . The whole kernel goes along with the missing value imputations in a very detailed and explorative manner . Since we have many features (both categorical and numerical) , the inter-relationship of each feature with any other is quite difficult and cumbersome to analyse . One of the feature's missing value imputation ( apart from Sale Price ) is done by modelling and prediction as a demo and rest using the cross-tabulations ,localizing mean , mode or median . Finally normalizing the matrix and predicting the values along with cross validation and root-mean-square error .

human-activity-recognition-with-neural-network-using-gyroscopic-and-accelerometer-variables

The VALIDATION ACCURACY is BEST on KAGGLE. Artificial Neural Network with a validation accuracy of 97.98 % and a precision of 95% was achieved from the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this: The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.

hyperas

Keras + Hyperopt: A very simple wrapper for convenient hyperparameter optimization

imageai

A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

kaggle-contests

Solutions for various datasets and contests on Kaggle

keras-gan

Keras implementations of Generative Adversarial Networks.

lstm-human-activity-recognition

Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN (Deep Learning algo). Classifying the type of movement amongst six activity categories - Guillaume Chevalier

lstm_anomaly_thesis

Anomaly detection for temporal data using LSTMs

machine-learning-tutorials

machine learning and deep learning tutorials, articles and other resources

mask_rcnn_humanpose

Mask R-CNN for Human Pose Estimation on Keras and TensorFlow.

medical-imaging-datasets

A list of Medical imaging datasets. Source : https://sites.google.com/site/aacruzr/image-datasets