A humple implementation of the NeuroEvolution of Augmenting Topologies[NEAT] algorithm written purely in Python3.
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Updated
Jun 27, 2024 - Python
A humple implementation of the NeuroEvolution of Augmenting Topologies[NEAT] algorithm written purely in Python3.
Predict number of JIRA bugs in SSD engineering dev process as a proxy for market readiness
Advance Deep learning with Model Implementation ANN && CNN (working.....)
This project utilizes a CNN model to classify cat and dog images through training and testing processes. The model is created using the Keras library on the TensorFlow backend.
This program implements logistic regression from scratch using the gradient descent algorithm in Python to predict whether customers will purchase a new car based on their age and salary.
This project provided practice with logistic regression and the cost functions MSE and log loss
유전알고리즘과 인공신경망을 활용허여 마리오 학습
Neural Networks from scratch (Inspired by Michael Nielsen book: Neural Nets and Deep Learning)
Avoiding the vanishing gradients problem by adding random noise and batch normalization
A fully-functioning logistic regression model using only python and numpy.
Python logistic regression (using a perceptron) classifier to recognize cats.
Global empirical models for tropopause height determination
Development of a Neural Network from scratch to predict divorce in marriages.
Using Logistic Regression Classifier to Predict Target Using Three Features
Basic level implementation of Neural Network
Popular predictive models and optimizers implemented in pure Python.
An abstract implementation of a Multilayered Perceptron (Multilayered Feedforward Neural Network). Provides a well-documented API which exposes a wrapper around the whole process (all the way from network config, to modelling, to training and predicting). Built in Python.
A simple program in python to illustrate the workings of a neural network
A simple sigmoid activation function-based neural network using the delta rule.
Push features to OSM taked from satellite images.
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