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code_snippets

connected-component

Connected Components on Apache Spark - Map Reduce Implementation

convolutional_neural_network

This is the code for "Convolutional Neural Networks - The Math of Intelligence (Week 4)" By Siraj Raval on Youtube

data-aggregation-big-data-analysis-and-visualization

• Aggregated data from following sources suing the respective application programing interface(API). 1. NY Times 2. Twitter 3. Common Crawl - Applied classical big data analytic method of Map Reduce to the unstructured data collected by the above mentioned sources. - Stored the data collected on WORM infrastructure Amazon Web Services (AWS) S3 AND EC2. - Visualized using Tableau to deduce trends in the processed Data

data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

dcgan-cifar10

A implementation of DCGAN (Deep Convolutional Generative Adversarial Networks) for CIFAR10 image

deep-learning

Repo for the Deep Learning Nanodegree Foundations program.

deepdream

fast-food-billing-program

fizzbuzznn

Solving the FizzBuzz problem using Neural Networks

generative-adversarial-network-tutorial

Tutorial on creating your own GAN in Tensorflow

groupmessenger1

Designed a fault-tolerant group messenger using Java Socket API, that can send message to multiple AVDs and store them in a permanent key-value storage.

ios-firstapp

My first iOS app

lab1-data-collection-and-eda

Collected data from sources like Twitter, NY Times and performed Exploratory Data Analysis that involves replicating professional data analysis on a topic of current interest, extending the data exploration to include another public data source. Collected and consolidated Twitter data using R. Integrated R with Google Maps to plot tweets. Used K-Means clustering to group tweets by proximity to identify the regions with highest tweet density.

python-machine-learning-book

The "Python Machine Learning (1st edition)" book code repository and info resource

rl-cheatsheet

RL Notation and Pseudocode

simpledynamo

Implemented a simplified version of Dynamo with key-value storage that provides availability and linearizability Handled node failures using concepts of Membership and Quorum Replication The main pieces of this project were Partitioning, Replication and Failure Handling Key features includes: • Membership • Request routing • Data replication • Data partitioning • Failure handling Following assumptions are made in this project: 1. There will be at most one app instance failure at any given time. 2. There are 5 nodes in the system. 3. Each node knows its position in the ring and about every other node in the ring. 4. There are no virtual nodes in the ring, unlike Amazon Dynamo. 5. It does not implement hinted handoff. 6. There are only two replicas stored in two consecutive nodes.

simplemessengerapp

A simple messenger app on Android. The goal of this app is enabling two Android devices to send messages to each other using Socket Programming.

super-resolution-using-gan

teacher-s-feedback-system

Platform independent tool for evaluating performance of teachers by students. Reports can be mailed to the respective teacher.