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awesome-artificial-intelligence

A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers

bert

TensorFlow code and pre-trained models for BERT

boston-house-prediction

Machine Learning Engineer Nanodegree Model Evaluation & Validation Project: Predicting Boston Housing Prices Welcome to the first project of the Machine Learning Engineer Nanodegree! In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with 'Implementation' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully! In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide. Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. In addition, Markdown cells can be edited by typically double-clicking the cell to enter edit mode.

cifar10-data-analysis

The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.

combined-cycle-power-plant

The dataset contains 9568 data points collected from a Combined Cycle Power Plant over 6 years (2006-2011), when the power plant was set to work with full load. Features consist of hourly average ambient variables Temperature (T), Ambient Pressure (AP), Relative Humidity (RH) and Exhaust Vacuum (V) to predict the net hourly electrical energy output (EP) of the plant. A combined cycle power plant (CCPP) is composed of gas turbines (GT), steam turbines (ST) and heat recovery steam generators. In a CCPP, the electricity is generated by gas and steam turbines, which are combined in one cycle, and is transferred from one turbine to another. While the Vacuum is colected from and has effect on the Steam Turbine, he other three of the ambient variables effect the GT performance. For comparability with our baseline studies, and to allow 5x2 fold statistical tests be carried out, we provide the data shuffled five times. For each shuffling 2-fold CV is carried out and the resulting 10 measurements are used for statistical testing.

flask-api

google-play-scraper

Google play scraper for Python inspired by <facundoolano/google-play-scraper>

hallo-world

intermediatepython

library-management-system

A Simple Library management system in which an admin and perform CRUD part and students can only view the list of books.

my-blog

myblog

Assignment

nlp-topic-modeling-and-sentiment-analysis-of-luxury-car--reviews

Using NLP and LDA for Topic Modeling and Sentiment Analysis

tensorflow-course

Simple and ready-to-use tutorials for TensorFlow

titanic-data-analysis

to-do-list

twitter-sentiment-analysis

Sentiment Analysis is the automated process of analyzing text data and sorting it into sentiments positive, negative or neutral. With more than 321 million active users, sending a daily average of 500 million Tweets, Twitter allows businesses to reach a broad audience and connect with customers without intermediaries.