<케라스 창시자에게 배우는 딥러닝 2판> 도서의 코드 저장소

R interface to Keras Tuner

<개발자를 위한 머신러닝&딥러닝> 도서의 코드 저장소

Simple integration of keras-tuner (hyperparameter tuning) and tensorboard dashboard (interactive visualization).

Extension for keras tuner that adds a set of classes to implement cross validation techniques.

In this repository I have utilised 6 different NLP Models to predict the sentiments of the user as per the twitter reviews on airline. The dataset is Twitter US Airline Sentiment. The best models each from ML and DL have been deployed. It employs text preprocessing,

Training neural networks to classify network traffic by L7 protocol.

This is sample repos for how to use Keras Tuner to perform hyper-parameter tuning in Databricks.

Using Hybrid model based on LSTM we predict the daily closing price of the index based on the historical data available. Using KerasTuner These models were trained, and automatically evaluating different components and design decisions, and their results were measured. Finally, we analyzed and clustered our results in order to know the character…

A machine learning solution for automating nucleus detection in biomedical images, leveraging the U-Net architecture to accelerate medical research and disease treatment discovery.

Final project of "Image Processing for Computer Vision" course.

This repository contains demo implementations for using keras tuner to tune hyperparameters of models in keras and scikitlearn. Additionally, it includes how to generate the visualization in Tensorboard.

Recommender systems became one of the essential areas in the machine learning field. Product recommendations are key to enhance customer exeperiance and help them to find the right product from huge corpus of products. When customer find the right product that are mostly like going to add the item to cart and which help in company revenue.

Convolutional Neural Network Architecture to classify Bone Fractures from X-Ray Images

Deep learning models to detect cyber attacks based on the paper "Towards Developing Network forensic mechanism for Botnet Activities in the IoT based on Machine Learning Techniques"

Our project leverages Python, pandas, Tableau, and machine learning techniques to analyse and predict student outcomes in higher education. Using a comprehensive dataset, we employ data preprocessing, visualisation with Tableau, and advanced machine learning models built with Python to uncover insights into graduation rates and factors influencing

[Python | NumPy | Pandas | Matplotlib | Seaborn | Plotly | TensorFlow | Keras Tuner | Scikit-learn | Streamlit | Heroku] Data science and machine learning project with online dashboard. It detects a possible brain tumor from an MRI scan.

Utilized deep learning systems to classify brain MRI scans into glioma tumor, meningioma tumor, pituitary tumor, or no tumor. We addressed class imbalance using undersampling and augmented the dataset with rotation, shifting, shearing, zooming, and flipping techniques.

Neural Network experimentation on the CIFAR-10 dataset ( https://www.cs.toronto.edu/~kriz/cifar.html )

Analysis of over 34,000 businesses that received funding, to generate 184 Neural Network algorithm to predict effective allocation of funding.