prakhardogra921 Goto Github PK

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Name: Prakhar Dogra

Type: User

Location: Delhi, India

Blog: http://mason.gmu.edu/~pdogra/

Prakhar Dogra's Projects

amazon-review-classification

Impementing a k-Nearest Neighbor Classifier to predict the sentiment for 18506 reviews for baby products provided.

anomaly-detection-on-signal-data

Implement the StrOUD algorithm, using LOF and KNN as the strangeness function to find anomalous data in the signal dataset.

autoencoders-using-nn-and-cnn

Autoencoders using simple Neural Networks and Convolutional Neural Networks. Also demonstrating that CNNs can be used for image denoising.

Bank Marketing Classification using scikit-learn library to train and validate classification models like Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, Neural Network and Support Vector Machine. Also conducted comparative study on the above models when applied on different feature sets obtained via feature selection (Chi-Square Test), feature transformation (Principal Component Analysis) and feature elimination.

bank-marketing-classification-using-several-pattern-recognition-methods

Bank Marketing Classification using Logistic Regression, Ada Boost, Random Forests, Radial Basis Function and Support Vector Machine

batch-normalization

Batch Normalization using tf.layers and tf.nn libraries. Also comparing the performances with and without batch normalization.

boston-housing

Boston House Price Prediction

character-wise-prediction-using-lstm

Character-wise prediction using LSTM. Implemented using Tensorflow.

clustering-analysis-on-customers-of-a-wholesale-distributor

Project in which unsupervised learning techniques are applied on product spending data collected for customers of a wholesale distributor in Lisbon, Portugal to identify customer segments hidden in the data.

clustering-using-kmeans-cmeans-and-gmm

Clustering using K Means, Fuzzy C Means and Gaussian Mixture Model

credit-card-fraud-detection

Credit Card Fraud Detection using various Anomaly Detection Algorithms and Techniques

deep-convolutional-generative-adversarial-network

Implement the Deep Convolutional GAN model to generate full color images. Trained the DCGAN model on the Street View House Numbers dataset.

deep-q-learning

Tensorflow implementation of a deep neural network that can learn to play games via reinforcement learning. For this notebook the game used is the Cart-Pole game that is available in the OpenAI Gym library. Also the deep neural network can be used for other games as well. In order to run this on your system you will also need to clone the OpenAI gym repository.

dog-breed-classifier

Dog Breed Classifier

dynamic-programming-for-reinforcement-learning

Implementations of many classical dynamic programming algorithms for updating policies of an environment via policy improvement and value iteration procedures. The environment used for this notebook is the Frozen Lake Environment (can be seen in the OpenAI Gym library) and the functions can be used for other environments as well.

face-generation

Using generative adversarial networks to generate new images of faces. Also tested the GAN with MNIST dataset.

find-donors-for-charity

Identifying different algorithms that could best find donors for charity

finding-similar-images-using-locality-sensitive-hashing

Finding Similar Images using Locality Sensitive Hashing (using Min Hashing and Random Hyperplanes technique)

general-adversarial-network-mnist

Building our own GAN and train it on MNIST dataset. Built the generator and discriminator networks using Tensorflow.

generating-tv-scripts-using-lstm

Generating our own Simpsons TV scripts using LSTM. Used part of the Simpsons dataset of scripts from 27 seasons. The Neural Network built will generate a new TV script for a scene at Moe's Tavern. Implemented using Tensorflow.

google-search

Verification

handwritten-digit-recognition-using-mlp

Hand-Written Digit Recognition using MLP (Multi-Layer Perceptron). Implemented using Keras library.

image-augmentation-on-cifar-10-dataset

Image Augmentation on CIFAR-10 Dataset to improve classification accuracy

image-classification-on-cats-and-dogs

Image Classification on Cats and Dogs

image-classification-on-cifar-10-dataset

Image Classification on CIFAR-10 Dataset using CNN (Convolutional Neural Networks) and MLP (Multi-Layer Perceptron)

image-styling

Style Transfer of one image to another

machinelearningoctavecodes

Linear Regression, Logistic Regression, Neural Networks and Polynomial Regression

monte-carlo-methods-for-reinforcement-learning

Implementations of many Monte Carlo (MC) algorithms for updating policies of an environment using action values, greedy and epsilon-greedy procedures. Environment used for this notebook is the BlackJack Environment (can be seen in the OpenAI Gym library) and these functions can be used for other environments as well.

movie-pair-analysis-using-hadoop-and-spark

Implementing Pairs and Stripes approach to find pairs with high co-occurrence frequency, conditional probability and Lift

movie-recommender-system-in-spark

Implementing a Recommender System in Spark to predict ratings of movies. Utility matrix is based on the Movie Lens dataset. Using the Products of Factors technique for the system and optimizing the loss function with ALS.

prakhardogra921 Goto Github PK

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