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500 AI Machine learning Deep learning Computer vision NLP Projects with code
Repository of various AI models - LSTM, CNN, AutoEncoders, GAN and Reinforcement Learning
This is Andrew NG Coursera Handwritten Notes.
Anomaly detection related books, papers, videos, and toolboxes
Fake Data + Real Data = CGAN
Learning hyperparameters for unsupervised anomaly detection
Nvidia DLI workshop on AI-based anomaly detection techniques using GPU-accelerated XGBoost, deep learning-based autoencoders, and generative adversarial networks (GANs) and then implement and compare supervised and unsupervised learning techniques.
Applied Machine Learning Explainability Techniques, published by Packt
Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction
Attention-Based Deep Learning model for performing fusion of Optical Remote Sensing Images (Low Resolution Multi-Spectral Image (LRMS) and Panchromatic image (PAN)) to generate a High Resolution Multi-Spectral Image (HRMS)
auto_LiRPA: An Automatic Linear Relaxation based Perturbation Analysis Library for Neural Networks and General Computational Graphs
A curated list of automated deep learning (including neural architecture search and hyper-parameter optimization) resources.
Curating a list of AutoML-related research, tools, projects and other resources
Awesome Incremental Learning
Selected Paper from the AI-CyberSec 2021 Workshop in the 41st SGAI International Conference on Artificial Intelligence (MDPI Journal Electronics)
Emsembling Text and Structured Nerual Network models
Classical-Quantum hybrid model for credit card fraud detection
This code uses the income data set to predict whether a given data point has an income below or above $50k. This code also includes data visualization, Data preprocessing, and tuning various hyper parameters to see if the model performs better. The Code follows the following order: Loading the dataset Statistical information retrieved from the dataset Data visualizations Data Pre processing. (Normalization, One hot encoding, handling missing/corrupted values and outliers) Train test split Building a Neural Network. The tuning hyper parameters executed are as follows: Modelling it with different activation functions Changing the Drop out value Modelling the optimizer Adding early stopping Adding regularizations Defining a gradient clipping Performing K-fold cross validation
A jupyter notebook for binary classification of breast cancer using XGBoost with Bayesian optimization.
we will use a neural network called an autoencoder to detect fraudulent credit/debit card transactions on a Kaggle dataset. We will introduce the importance of the business case, introduce autoencoders, perform an exploratory data analysis, and create and then evaluate the model. The model will be presented using Keras with a TensorFlow backend usi
This Project is for Web Dev Internship NITT 2023
Modified XGBoost implementation from scratch with Numpy using Adam and RSMProp optimizers.
A helpful 5-page machine learning cheatsheet to assist with exam reviews, interview prep, and anything in-between.
Data Mining - EDA, Feature Selection, Standardize, Remove Global Outliers, Normalize, Feature Extraction (with PCA), Clustering, Classification (baseline models and hyperparameter tuning with GridSearchCV).
Convolutional and LSTM networks to classify human activity
A series of Jupyter notebooks that walk you through NNs, CNNs, RNNs, LSTMs, GANs in python using Scikit-Learn and TensorFlow.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.