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Recommendation-Engine-with-KNN-algorithm
Collaborative Filtering , Fuzzy LogicWeather-App
Node.js ProjectCNN-Classification
CNN Architecture- using Data Augmentation & Classify ImagesTensorboard-Tutorial
It gives the Visualization of whole model- Graphs, Computational graphs, Histograms , Distributions and Metric Evaluation too.Neural-Style-Transfer
Amazing Loss Function IntuitionSTOCK--Price-Prediction
Prediction of Stock Price using LSTMConvolutional-Autoencoder
Fashion MNIST DatasetCLAHE
Contrast limited adaptive histogram equalization (CLAHE)Movie-Recommendation-System
Movie Recommendation System using Top 10 recommended movies (Content based Filtering) using NLPImage-Denoising-Autoencoder
Keras Denosing Autoencoder on MNIST Dataset using Conv2DTranspose (additional-tool)COMPUTER-VISION--Practice
All practice modulesImage-Operations-and-Edge-Filters
Channelising and basic conversions between the 3 (RGB) channelsSVM
Support Vector MachinesMovie-Recommender-System--LightFM
Movie Recommender system using LightFM libraryTime-Series-Forecasting-
Practice ModelCOVID-19-Project
Advanced Visualizations of World Wide spread of Novel Corona Virus using World Map tracking for various countries - India, Italy , South Korea and China(Wuhan)Tuner-with-Fashion-MNIST
Using Tuner Search for Hyperparameter OptimisationStock-Sentiment-Analysis
NLP based analysis of Kaggle dataset: Stock-HeadlinesNatural-Language-Processing
All the features of NLPLSTM--1
Kaggle Dataset PracticeDigital-Image-Processing-
Doing Various Techniques.SMS-Spam-Classifier-
Stack: NLP, Naive Bayer Classifier | Accuracy : 98.6%Spam-Classifier-APP
ML app with FlaskFaceNet-Model
TestingTweet-Sentiment-Analysis
Tweet Sentiment AnalysisChurn-Model
Accuracy of 86% predicting whether the customer continues with the bank or not based on various factors and transactions using XGBOOST (RandomisedSearchCV)UNet-Autoencoder
U-Net is a convolutional neural network that was developed for biomedical image segmentation at the Computer Science Department of the University of Freiburg, Germany. The network is based on the fully convolutional network[2] and its architecture was modified and extended to work with fewer training images and to yield more precise segmentationsVariational-Autoencoder
Great Article for Reference: https://www.machinecurve.com/index.php/2019/12/30/how-to-create-a-variational-autoencoder-with-keras/Devanagari-Character-Recognition
The DHCD (Devnagari Character Dataset) of handwritten digits. It consists 46 characters from ΰ€ to ΰ€ΰ₯ΰ€ and ΰ₯¦ to ΰ₯―.Love Open Source and this site? Check out how you can help us