Artificial neural network classes and tools in Python and TensorFlow.
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Updated
Oct 1, 2020 - Python
Artificial neural network classes and tools in Python and TensorFlow.
Python Machine Learning Algorithms
Code created for blog series on unsupervised feature/topic extraction from corporate email content. An implementation for cleaning raw email content, data analysis, unsupervised topic clustering for sentiment/alignment and ultimately several deep-learning models for classification. Details at www.avemacconsulting.com.
Example of deploying a machine learning model as a Python web app on Heroku
Python Machine Learning Examples used for Teaching
This Repo Contains Example Files For Image Filtering Using Python
Project data science Python membuat rekomendasi film menggunakan metode Machine Learning
NN and CNN implementation from scratch in PyTorch with data loading, training, and evaluation phase on CIFAR 100 image dataset
Real-Time Stock Price Visualization using Streamlit
STUDY ON PROCESSING BRAIN SIGNALS USING EEG SENSOR BY MACHINE LEARNING
Diabetes detection with ML and API integration.
⚡Explore machine learning concepts through Python scripts and Jupyter notebooks showcasing algorithms and applications
A repository for machine learning and artificial intelligence projects applied to games
Classic ML starter project to classify messages from a sms dataset into spam and ham
Quick image classification model designed to tell the difference between an airplane, boat, or a car that was trained on hundreds of images!
Color Quantization using K-Means clustering algorithm and Python's OpenCV library - Artificial Intelligence (AI) final project - Summer 2024 /// by theMHD; thanks for chatGPT :)
Rice disease prediction api with trained ML model.
Fertilizer prediction Api (https://predict-fertilizer-api.onrender.com/predict)
EN: Machine-Learning Python Application with included GUI / RO: Aplicatie python care foloseste machine-learning pentru a determina lucruri specifice aplicatiei, ilustrata cu ajutorul unui friendly UI.
Various supervised machine learning techniques on the highly optimized NSL-KDD dataset to create an efficient and accurate predictor of possible intrusions on a network.
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