Pinned Repositories
2048_Game
2048 game with variables no. of boxes feature
Decision-Tree-Iris-Dataset-
A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.
DNA-GC-Content
Emojis-Detection
Facial-Detection---OpenCv
The objective of the program given is to detect object of interest(face) in real time and to keep tracking of the same object.This is a simple example of how to detect face in Python. You can try to use training samples of any other object of your choice to be detected by training the classifier on required objects.
Hindi-Alphabets-Recognition
Hindi Alphabets Recognition
Language-Detection
Language Detection(NLTK)
Machine-Learning-Algorithms
Self-Driving-Car-using-using-Udacity-Simulator
In this Project i have using udacity simulator to train my dense Keras model by using Training mode
Sound-Feature-Extraction
Priyanshuuu's Repositories
Priyanshuuu/Decision-Tree-Iris-Dataset-
A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.
Priyanshuuu/Facial-Detection---OpenCv
The objective of the program given is to detect object of interest(face) in real time and to keep tracking of the same object.This is a simple example of how to detect face in Python. You can try to use training samples of any other object of your choice to be detected by training the classifier on required objects.
Priyanshuuu/DNA-GC-Content
Priyanshuuu/Emojis-Detection
Priyanshuuu/Hindi-Alphabets-Recognition
Hindi Alphabets Recognition
Priyanshuuu/Machine-Learning-Algorithms
Priyanshuuu/Sound-Feature-Extraction
Priyanshuuu/Attendance-API
Priyanshuuu/Beauiful-Soop-Python
Priyanshuuu/Breast-Cancer-Linear-Regression-
Priyanshuuu/Data-Structures-Algo
Priyanshuuu/Demonetization-Analysis
Priyanshuuu/Digits-classifier
Priyanshuuu/errors
Priyanshuuu/GSoC
A guide for participating in Google Summer of Code with Sugar Labs
Priyanshuuu/HacktoberFest-HelloWorld
All your PRs will be merged !! 😊
Priyanshuuu/Image-Caption-Generator
Priyanshuuu/Income-Tax-Fraud-Detection
Priyanshuuu/microservices-101
Starter application for microservices architecture with NodeJS (Enterprise Tech Meetup)
Priyanshuuu/Mnist-Analysis
Priyanshuuu/Movies-Reviews---NLTK
Priyanshuuu/Notes.ai
A project to practice node , express, mongoDb skills
Priyanshuuu/open_model_zoo
Pre-trained Deep Learning models and samples (high quality and extremely fast)
Priyanshuuu/Priyanshuuu
Priyanshuuu/Project-Cifar10
The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes. The 10 different classes represent airplanes, cars, birds, cats, deer, dogs, frogs, horses, ships, and trucks. There are 6,000 images of each class. Computer algorithms for recognizing objects in photos often learn by example. CIFAR-10 is a set of images that can be used to teach a computer how to recognize objects. Since the images in CIFAR-10 are low-resolution (32x32), this dataset can allow researchers to quickly try different algorithms to see what works. Various kinds of convolutional neural networks tend to be the best at recognizing the images in CIFAR-10. CIFAR-10 is a labeled subset of the 80 million tiny images dataset. When the dataset was created, students were paid to label all of the images.
Priyanshuuu/samplerun
Priyanshuuu/Scikit-Learn
Priyanshuuu/Stone-Paper-Scissor-Game
Stone Paper Scissor Game
Priyanshuuu/Text-Classification-NLP
The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. To the best of my knowledge, it was originally collected by Ken Lang, probably for his Newsweeder: Learning to filter netnews paper, though he does not explicitly mention this collection. The 20 newsgroups collection has become a popular data set for experiments in text applications of machine learning techniques, such as text classification and text clustering.
Priyanshuuu/Zika-RNAseq-Pipeline
An open RNA-Seq data analysis pipeline tutorial with an example of reprocessing data from a recent Zika virus study