Ayush Chauhan's Projects
A resource repository for 3D machine learning
Searching and Sorting Algorithms .
python implementations of Analyzing Neural Time Series Textbook
Artificial Neural Networks in Python
Algorithm implementations and homework solutions for the Stanford's online courses
ANN and Deep learning examples
:memo: An awesome Data Science repository to learn and apply for real world problems.
Working on ETL & ELT pipelines using Azure Data Factory for Batch & streaming flow of Structure & Un-structured data.
Essential Cheat Sheets for deep learning and machine learning researchers
Work done in Slide Rule's Data Science Intensive course.
Continually updated data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
Useful Data Science and Machine Learning Tools,Libraries and Packages
Blog @ Medium
An open-source storage layer that brings scalable, ACID transactions to Apache Spark™ and big data workloads.
The model uses the computer webcam to calculate a CI based on a composite score of emotional state and eye/head movement and displays a real-time Engagement and Emotional Classification.
Udacity Machine Learning Nanodegree Final Project
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 .
Data from EDGAR filling was extracted and text analysis was performed.
This project is for classification of emotions using EEG signals recorded in the DEAP dataset to achieve high accuracy score using machine learning algorithms such as Support vector machine and K - Nearest Neighbor.
Used LSTM Network to classify eeg signals based on stimuli the subject recieved (visual or audio)
Creating a C# App with Visual Studio to produce Events and through that in Azure Event App.
This project will Produce Events to Azure Cosmos Db & then Capture the Same in Azure Event Hub through Azure Function
Detects Face using Haarcascades and further detects emotion in bounded face (trained a CNN emotion detector model)
Context This dataset contains tree observations from four areas of the Roosevelt National Forest in Colorado. All observations are cartographic variables (no remote sensing) from 30 meter x 30 meter sections of forest. There are over half a million measurements total! Content This dataset includes information on tree type, shadow coverage, distance to nearby landmarks (roads etcetera), soil type, and local topography. Acknowledgement This dataset is part of the UCI Machine Learning Repository, and the original source can be found here. The original database owners are Jock A. Blackard, Dr. Denis J. Dean, and Dr. Charles W. Anderson of the Remote Sensing and GIS Program at Colorado State University. Inspiration Can you build a model that predicts what types of trees grow in an area based on the surrounding characteristics? A past Kaggle competition project on this topic can be found here. What kinds of trees are most common in the Roosevelt National Forest? Which tree types can grow in more diverse environments? Are there certain tree types that are sensitive to an environmental factor, such as elevation or soil type?
This is a Python script of the classic game "Hangman". The word to guess is represented by a row of dashes. If the player guess a letter which exists in the word, the script writes it in all its correct positions. The player has 6 turns to guess the word. You can easily customize the game by changing the variables.
Samples of Harvest API usage in various languages.
K Means Clustering Project Using KMeans Clustering to cluster Universities into to two groups: Private and Public. The Data Data frame has 777 observations on the following 18 variables. Private A factor with levels No and Yes indicating private or public university Apps Number of applications received Accept Number of applications accepted Enroll Number of new students enrolled Top10perc Pct. new students from top 10% of H.S. class Top25perc Pct. new students from top 25% of H.S. class F.Undergrad Number of fulltime undergraduates P.Undergrad Number of parttime undergraduates Outstate Out-of-state tuition Room.Board Room and board costs Books Estimated book costs Personal Estimated personal spending PhD Pct. of faculty with Ph.D.’s Terminal Pct. of faculty with terminal degree S.F.Ratio Student/faculty ratio perc.alumni Pct. alumni who donate Expend Instructional expenditure per student Grad.Rate Graduation rate