Pinned Repositories
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
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.
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
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
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.
Satnam00's Repositories
Satnam00/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.
Satnam00/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.
Satnam00/Classifying-One-Dimensional-Images-PyTorch
# Image Classification using Logistic Regression in PyTorch
Satnam00/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
Satnam00/basics-of-keras
Satnam00/Basics-of-PyTorch-
It contains the basic code of PyTorch
Satnam00/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.
Satnam00/Data-Prepocessing
Satnam00/delete
Satnam00/Fake_News_Classifier_with_Keras
Satnam00/Feed_Forward_Neural_Network_PyTorch
Satnam00/flask
Satnam00/Generative-Adverserial-Networks-in-PyTorch-for-3D-images
Satnam00/Important-Notes
Satnam00/Market-Mix-Modeling
Satnam00/Medical-AI-Android
Satnam00/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.
Satnam00/NLP_Learning
Satnam00/OptimizeCNN_with_Keras_Tuner
Satnam00/optuna
A hyperparameter optimization framework
Satnam00/portfolio_2
Satnam00/pytextrank
Python implementation of TextRank for phrase extraction and summarization of text documents
Satnam00/sample
Satnam00/satnam.github.io
Satnam00/scrape-wordometer-website
Satnam00/sql_learning_excercises
Satnam00/stock_price
Satnam00/TFIDF_Tutorial
Satnam00/web-scraping-basics
Satnam00/YTshared
code share for youtube videos