Shakhaf Joseph's repositories
Fruits_Classifier_CNN
A high-quality, dataset of images containing fruits. The following fruits are included: Apples (different varieties: Golden, Golden-Red, Granny Smith, Red, Red Delicious), Apricot, Avocado, Avocado ripe, Banana (Yellow, Red), Cactus fruit, Cantaloupe (2 varieties), Carambula, Cherry (different varieties, Rainier), Cherry Wax (Yellow, Red, Black), Clementine, Cocos, Dates, Granadilla, Grape (Pink, White, White2), Grapefruit (Pink, White), Guava, Huckleberry, Kiwi, Kaki, Kumsquats, Lemon (normal, Meyer), Lime, Lychee, Mandarine, Mango, Maracuja, Melon Piel de Sapo, Mulberry, Nectarine, Orange, Papaya, Passion fruit, Peach, Pepino, Pear (different varieties, Abate, Monster, Williams), Physalis (normal, with Husk), Pineapple (normal, Mini), Pitahaya Red, Plum, Pomegranate, Quince, Rambutan, Raspberry, Salak, Strawberry (normal, Wedge), Tamarillo, Tangelo, Tomato (different varieties, Maroon, Cherry Red), Walnut.
ML_text_classification_
Natural Language Processing for Text Classification with NLTK and Scikit-learn
seattle_airbnb_price_predictions
Since 2008, guests and hosts have used Airbnb to travel in a more unique, personalized way. As part of the Airbnb Inside initiative, this dataset describes the listing activity of homestays in Seattle, WA.
walmart_store_location_plot
Walmart Stores Location contains full information about each store. Current version is ideal for practicing data vizualization skills. It has really convenient data for building up graphs.
Animal_Classification
This convolutional neural network obtained state-of-the-art performance at object recognition on the CIFAR-10 image dataset in 2015. We will build this model using Keras, a high-level neural network application programming interface (API) that supports both Theano and Tensorflow backends. You can use either backend; however, I will be using Theano.
bertviz
Tool for visualizing attention in the Transformer model (BERT, GPT-2, Albert, XLNet, RoBERTa, CTRL, etc.)
Boilerplate-Webscraping
The repository contains the boiler plate code for we scrapping.
coursera-deeplearning-specialization
Homework from the deeplearning.ai Deep Learning Specialization on Coursera
Credit_card_fraud_detection
Throughout the financial sector, machine learning algorithms are being developed to detect fraudulent transactions. In this project, that is exactly what we are going to be doing as well. Using a dataset of of nearly 28,500 credit card transactions and multiple unsupervised anomaly detection algorithms, we are going to identify transactions with a high probability of being credit card fraud. In this project, we will build and deploy the following two machine learning algorithms: Local Outlier Factor (LOF), Isolation Forest Algorithm
machine_learning
Implementation of several machine learning algorithms