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basics-of-keras

basics-of-pytorch-

It contains the basic code of PyTorch

california-housing-price-prediction

Build a model of housing prices to predict median house values in California using the provided dataset. Train the model to learn from the data to predict the median housing price in any district, given all the other metrics. Predict housing prices based on median_income and plot the regression chart for it.

classifying-one-dimensional-images-pytorch

# Image Classification using Logistic Regression in PyTorch

credit-card-fraudlent

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.

data-prepocessing

data-structure

Learn awesome data structure !!!

delete

fake_news_classifier_with_keras

feed_forward_neural_network_pytorch

flask

generative-adverserial-networks-in-pytorch-for-1d-images

Deep neural networks are used mainly for supervised learning: classification or regression. Generative Adverserial Networks or GANs, however, use neural networks for a very different purpose: Generative modeling Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset.

generative-adverserial-networks-in-pytorch-for-3d-images

house-prices-advanced-regression-techniques

The Ames Housing dataset was compiled by Dean De Cock for use in data science education. It's an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset.

hyperparameter-optimization

Models can have many hyperparameters and finding the best combination of parameters can be treated as a search problem and the best technique to find the same is called hyperparameter tuning

important-notes

k_means_algorithem

Kmeans algorithm is an iterative algorithm that tries to partition the dataset into Kpre-defined distinct non-overlapping subgroups (clusters) where each data point belongs to only one group. It tries to make the inter-cluster data points as similar as possible while also keeping the clusters as different (far) as possible. It assigns data points to a cluster such that the sum of the squared distance between the data points and the cluster’s centroid (arithmetic mean of all the data points that belong to that cluster) is at the minimum. The less variation we have within clusters, the more homogeneous (similar) the data points are within the same cluster.

linear-regression

Simple linear regression is useful for finding relationship between two continuous variables. One is predictor or independent variable and other is response or dependent variable. It looks for statistical relationship but not deterministic relationship. Relationship between two variables is said to be deterministic if one variable can be accurately expressed by the other. For example, using temperature in degree Celsius it is possible to accurately predict Fahrenheit. Statistical relationship is not accurate in determining relationship between two variables. For example, relationship between height and weight.

linear-regression-using-pytorch

The learning part of linear regression is to figure out a set of weights w11, w12,... w23, b1 & b2 by looking at the training data, to make accurate predictions for new data (i.e. to predict the yields for apples and oranges in a new region using the average temperature, rainfall and humidity). This is done by adjusting the weights slightly many times to make better predictions, using an optimization technique called gradient descent

market-mix-modeling

medical-ai-android

movielens-content-based-recommendation-engine-for-movies

This repo shows a set of Jupyter Notebooks demonstrating a variety of movie recommendation systems for the MovieLens 1M dataset. The dataset contain 1,000,209 anonymous ratings of approximately 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000.

nlp_learning

optimizecnn_with_keras_tuner

optuna

A hyperparameter optimization framework

osic-pulmonary-fibrosis-progression-predict-lung-function-decline

In this competition, we’ll predict a patient’s severity of decline in lung function based on a CT scan of their lungs. We’ll determine lung function based on output from a spirometer, which measures the volume of air inhaled and exhaled.

password_based_system_on_keil_with_proteus_file

Passwords provide security mechanism for authentication and protection services against unwanted access to resources. A graphical based password is one promising alternatives of textual passwords. According to human psychology, humans are able to remember pictures easily. In this paper, we have proposed a new hybrid graphical password based system, which is a combination of recognition and recall based techniques that offers many advantages over the existing systems and may be more convenient for the user. Our scheme is resistant to shoulder surfing attack and many other attacks on graphical passwords.

patient-has-diabetis-or-not

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective of the dataset is to diagnostically predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage.

portfolio_2

pytextrank

Python implementation of TextRank for phrase extraction and summarization of text documents