epochs
There are 39 repositories under epochs topic.
Tempo.jl
Efficient Astronomical Time transformations in Julia.
Baby-Health-Data-Science
Baby Health model made in Python.
EEG_to_ERP_pipeline_stats_R
General pipeline used for analyzing EEG data where Raw EEG data gets transformed into ERPS and Stats are done in R (Mixed effects models)
persistence-sdk
Node modules and client utilities to build Persistence platform node applications.
Hand-Gesture-Recognition-Rock-Paper-Scissor
Hand Gesture Recognition and Modification was based on transfer learning Inception v3 model using Keras with Tensorflow backend trained on 4 classes - rock, paper, scissors, and nothing hand signs. The final trained model resulted in an accuracy of 97.05%. The model was deployed using Streamlit on Heroku Paas.
Neural_Networks_Forest_Fire_Classification
PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS
image-classification-and-manipulation-in-python-machine-learning
image classification and manipulation in python machine learning on fashion mnist dataset
scorepochs_py
scorEpochs: a computer aided scoring tool for resting-state M/EEG epochs
GazeBasedEpochSelection
Gaze data -based epoch selection algorithm for eye tracker assisted visual evoked potential paradigm
Cyclical-Learning-Rates-for-Training-Neural-Networks-With-Unbalanced-Data-Sets
As the learning rate is one of the most important hyper-parameters to tune for training convolutional neural networks. In this paper, a powerful technique to select a range of learning rates for a neural network that named cyclical learning rate was implemented with two different skewness degrees. It is an approach to adjust where the value is cycled between a lower bound and upper bound. CLR policies are computationally simpler and can avoid the computational expense of fine tuning with fixed learning rate. It is clearly shown that changing the learning rate during the training phase provides by far better results than fixed values with similar or even smaller number of epochs.
Neural_Networks
TensorFlow 2.2, Keras, Deep Learning
Investment_Predictions_Deep_Learning
This project creates a machine learning model that predicts the success of investing in a business venture.
tf_gan_handwritten_digits
A Generative Adversarial Network (GAN) that generates handwritten digits(0 to 9). Uses mnist dataset. Written in R
Employee-Attrition-and-Department-Prediction
The purpose of this project is to develop a machine learning model that predicts employee attrition (whether an employee will leave the company) and department assignment (which department an employee belongs to) based on various factors. These factors include age, travel frequency, education level, job satisfaction, marital status, and more.
LogisticRegressionTraining
This project involves training a machine learning model and plotting its learning curves to analyze training and testing accuracies, utilizing Java for model execution and Python for data visualization. It includes commands for compiling and running the Java program, generating plots, and sending results via email.
MLP-neural-network
A Multi Layer Perceptron created to calculate XOR (eXclusive OR) on 5 inputs
Artificial-Neural-Network
Artificial Neural Network using PyTorch & Keras Libraries
PyTorch-ANN-Model
PyTorch Library on Artificial Neural Network Model
NeuralNetwork_Projects
NN project to understand from scratch how internally Weights are applied, Epochs, Backpropagation, Error calculation, Gradient.
Image-Caption-Generator
Image caption generator project is automatically describes images with coherent and relevant textual captions.
Image_classification_using_the_CIFAR_10_dataset
The main concentration of this project lies on image calssification using traditional CNN(Convolution Neural Networks), and also a couple of "BASE MODELS" such as "RestNet50", "DenseNet121" and "EfficientNetB0" that upgraded the performance of our CNN, followed by the Fully Connected NN, that we are using to train our model on.
CNNImageClassifier
Using Convolutional Neural Networks to create image classifiers [ cats + dogs & cats + dogs + lions + tigers ]
Estimating-evoked-responses-of-EEG-using-MNE-python
Estimating evoked responses
Deep_Learning_Challenge
Module 21 Challenge
Neural_Network_Charity_Analysis
Neural_Network_Charity_Analysis
scorepochs_mat
Scorepochs: a computer aided scoring tool for resting-state M/EEG epochs
Scorepochs-tools
Scorepochs: a computer aided scoring tool for resting-state M/EEG epochs
EEG-processing
EEG data collection and processing in matlab. Proposed data collection algorithm and Processing pipeline for evoked potentials of EEG signals or regular EEG signals. Furthermore
Nerual-Network_Image-Data
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria.
Neural-Network-Logic-Gates
This repository provides the Implementation of logic gates using neural networks.
Convolutional-Neural-Network
Program implements a convolutional neural network for classifying images of numbers in the MNIST dataset as either even or odd using GPU framework.
neural-network-charity-analysis
Designing a deep learning neural network with TensorFlow to predict the success of donations
Chest-X-Ray-Deep-Learning-Project
This repository includes my Chest X-Ray Deep Learning-Flatiron School Module 4 Project. For this project, I made use of OS to access the data. The Pandas, NumPy, Matplotlib, Seaborn, and Plotly libraries were used to explore the data. Keras was used to build the image classifier.
NeuralNetwork-ModelBuilding-with-Keras-and-TensorFlowBackend
This repository focuses on building several versions of Deep Learning Neural Network Models (Sequential Model, Model with increase in hidden layers, Model with Regularization to avoid overfitting) with Keras that uses TensorFlow Back end.
Traffic-Sign-Recognition
Traffic sign recognition using CNN
Tom-and-Jerry-Emotion-Detection-Challenge
The predicts the emotion of the characters from image provided.