deadskull7's Stars
chrislgarry/Apollo-11
Original Apollo 11 Guidance Computer (AGC) source code for the command and lunar modules.
aleju/imgaug
Image augmentation for machine learning experiments.
albumentations-team/albumentations
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
soumith/ganhacks
starter from "How to Train a GAN?" at NIPS2016
eriklindernoren/Keras-GAN
Keras implementations of Generative Adversarial Networks.
twopirllc/pandas-ta
Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 150+ Indicators
qubvel/segmentation_models
Segmentation models with pretrained backbones. Keras and TensorFlow Keras.
DistrictDataLabs/yellowbrick
Visual analysis and diagnostic tools to facilitate machine learning model selection.
microsoft/hummingbird
Hummingbird compiles trained ML models into tensor computation for faster inference.
ieee8023/covid-chestxray-dataset
We are building an open database of COVID-19 cases with chest X-ray or CT images.
sfikas/medical-imaging-datasets
A list of Medical imaging datasets.
qubvel/efficientnet
Implementation of EfficientNet model. Keras and TensorFlow Keras.
achillesrasquinha/bulbea
:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling
jadianes/spark-py-notebooks
Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks
bckenstler/CLR
joeddav/devol
Genetic neural architecture search with Keras
harvitronix/neural-network-genetic-algorithm
Evolving a neural network with a genetic algorithm.
yohann84L/plot_metric
Python package to simplify plotting of metric like ROC curve, confusion matrix etc..
deadskull7/One-Stop-for-COVID-19-Infection-and-Lung-Segmentation-plus-Classification
✋🏼🛑 This one stop project is a complete COVID-19 detection package comprising of 3 tasks: • Task 1 --> COVID-19 Classification • Task 2 --> COVID-19 Infection Segmentation • Task 3 --> Lung Segmentation
Chinmayrane16/Recommender-Systems-with-Collaborative-Filtering-and-Deep-Learning-Techniques
Implemented User Based and Item based Recommendation System along with state of the art Deep Learning Techniques
Chinmayrane16/ReconNet-PyTorch
A non-iterative algorithm to reconstruct images from compressively sensed measurements.
jeffery-zhougang/DeeplabV3Plus-Keras-Retraining
使用自己的数据集retraining DeeplabV3+
mrc03/Housing-Prices-EDA-and-Regression-Models
The famous Housing Price Advanced Regression competition on Kaggle. The dataset contains of training and testing sets each with about 1.46K rows and 81 features pertaining to a house. I have first performed an exhaustive EDA to identify the underlying trends in the data. I have also removed outliers to make the regression models more robust. Also proper missing values treatment has been done with imputation being done wherever needed. Lastly I have deployed various regression models like Lasso,Ridge etc... from scikit and have also tuned their parameters from the GridSearchCV module. Finally achieved a RMSE of little more than 0.12 which is pretty decent.
mrc03/Movie-Reviews-NLTK-Sentiment-Analysis-
The Movie Reviews dataset. The dataset is imported from the NLTK libray. It has 1000 positive and 1000 negative reviews. I have first imported the dataset into a pandas data frame which makes it easier to do the processing. The next step is to analyze the (+) and ( - ) reviews. I have also preprocessed the dataset using Lemmatizing and other standard NLP techniques. To extract the features from the text I have used the Tfidf vectorizer from the scikit. Lastly I have used various modellig algos from scikit to train on this data.
mrc03/Gender-Recognition-by-Voice-Val.-Acc.-0.9908-
The Gender Recognition by Voice dataset from kaggle. The dataset consists of 3168 voice samples each of which has 20 different acoustic properties and the target variable is the 'gender' or the 'label'. I have done exhaustive EDA to analyze the data and the underlying trends. Also the outliers have been detected and removed for better performance. I have also done significant feature engineering by adding couple of new relevant features. Also I have normalized the data for better performance. Lastly I have used many classification algos. from the scikit to predict the 'gender' from the voice sample. For me SVM gives highest accuracy of about a little more than 99.1 %.
mrc03/Spooky-Author-Identification
The notebook on famous Kaggle competition : Spooky Author Identification. The task is to identify the authors from their respective texts or work. I have first cleaned and pre-processed the text using standard NLP techniques like tokenization , stemming or lemmatization , stop-word removal etc.... I have also tried to create some meta features or hand-crafted features based on the author writing pattern. Then I have used the traditional BOW approach with TFIDF Vectorizer and the Count Vectorizer and then deployed ML algos like LogisticRegression and Naive Bayes which are well suited for text data. For me tfidf on count vectorizer gave best results till now ; My submission scored a multi-class log loss of 0.46 on kaggle private LB which is quite decent.
mrc03/Topic-Modelling-using-LDA-and-LSA-in-Sklearn
I have performed topic modelling on the dataset : "A Million News Headlines' on the kaggle. I have first pre-processed and cleaned the data. Then I have used the implementations of the LDA and the LSA in the sklearn library. Also the distribution of words in a topic is shown.
mrc03/Word-Embeddings-in-Gensim-and-Keras
A simple implementation of word embeddings in Gensim and Keras libraries. I have implemented famous Word2Vec in Gensim library. As an alternative I have also used Keras embedding layer to generate the word embeddings.
mrc03/Amazon-Fine-Food-Reviews-Analysis
The famous Amazon fine food reviews dataset on Kaggle for text classification. I have performed sentiment analysis on the dataset using different techniques. Please see readme for details.
cadae/COVID-19-Infection-CT-Scan-Images-Segmentation