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leaky-relu

Here are 32 public repositories matching this topic...

lucaaslb / cnn-libras

Rede Neural Convolucional para reconhecimento de gestos em LIBRAS (Alfabeto) Projeto 01/2019 - Ciência da Computação (Universidade Anhembi Morumbi)

deep-learning tensorflow cnn artificial-intelligence convolutional-layers convolutional-neural-networks libras cnn-keras inteligencia-artificial keras-tensorflow leaky-relu cnn-classification

Updated May 18, 2020
Python

yaricom / TimeSeriesLearning

The project aimed to implement Deep NN / RNN based solution in order to develop flexible methods that are able to adaptively fillin, backfill, and predict time-series using a large number of heterogeneous training datasets.

deep-neural-networks deep-learning time-series recurrent-neural-networks pca-analysis rnn adagrad rmsprop leaky-relu adam-optimizer random-forest-regressor adamax

Updated Aug 16, 2016
Python

MrDavidYu / TF_Convolutional_Autoencoder

Convolutional autoencoder for encoding/decoding RGB images in TensorFlow with high compression ratio

tensorflow autoencoder rgb-image face-recognition convolutional-autoencoder celeba convolutional leaky-relu tensorflow-gpu celeba-dataset

Updated Jul 28, 2018
Python

AFAgarap / vanishing-gradients

Avoiding the vanishing gradients problem by adding random noise and batch normalization

deep-learning artificial-intelligence batch-normalization artificial-neural-networks gradients leaky-relu sigmoid-function relu activation-functions

Updated Mar 25, 2023
Python

abhilash1910 / GraphAttentionNetworks

This package is a Tensorflow2/Keras implementation for Graph Attention Network embeddings and also provides a Trainable layer for Multihead Graph Attention.

tf2 keras-tensorflow leaky-relu graph-attention-networks multihead-attention self-attention

Updated Sep 23, 2021
Python

volkansonmez / Neural_Networks_From_Scratch

Deep Learning Projects

neural-network stochastic-gradient-descent leaky-relu softmax-algorithm cifar10-classification

Updated Oct 13, 2021
Jupyter Notebook

prabormukherjee / Layers_creation

Creating your own custom layers(Leaky ReLU) with Keras

tensorflow keras ml dl leaky-relu custom-layer

Updated Sep 20, 2020
Jupyter Notebook

himasai97 / GANs

Building Generative Adversarial Networks

python pytorch generative-adversarial-network gan mnist batch-normalization tensors leaky-relu adam-optimizer sigmoid-function colab-notebook google-colaboratory relu-activation

Updated Mar 1, 2021
Jupyter Notebook

aluminarius / ReLU_Plus_Plus

ReLU++: A modified ReLU activation function with enhanced performance for deep learning models.

python machine-learning deep-learning neural-network tensorflow optimization keras pytorch artificial-intelligence leaky-relu relu activation-function efficient-neural-networks parametric-relu flexible-activation-function

Updated Mar 13, 2025

RPGupta30 / Activation-Functions

softmax tanh leaky-relu sigmoid-activation softsign

Updated Jun 23, 2020
Python

SameetAsadullah / Neural-Network-Implementation

Neural Network implemented with different Activation Functions i.e, sigmoid, relu, leaky-relu, softmax and different Optimizers i.e, Gradient Descent, AdaGrad, RMSProp, Adam. You can choose different loss functions as well i.e, cross-entropy loss, hinge-loss, mean squared error (MSE)

Updated Aug 15, 2022
Jupyter Notebook

mkgeiger / neural-network

Lightweight neural network library written in ANSI-C supporting prediction and backpropagation for Convolutional- and Fully Connected neural networks

neural-network image-classification convolutional-neural-network backpropagation adagrad fully-connected-network sigmoid tanh leaky-relu adam-optimizer categorical-cross-entropy softmax-layer relu-layer sgd-optimizer mean-square-error momentum-optimization-algorithm softsign glorot-uniform rmsprop-optimizer

Updated Feb 24, 2025
C

BasicDNN

mmaric27 / BasicDNN

Generic L-layer 'straight in Python' fully connected Neural Network implementation using numpy.

Updated May 29, 2021
Python

Iheuzio / TickerPricePrediction

The code implements a neural network model, PricePredictor, trained on historical stock price data to predict future stock prices, visualizing the predictions alongside historical prices and calculating the average of the predicted prices.

finance deep-neural-networks analytics jupyter-notebook stocks linear-models loss-functions leaky-relu price-prediction-model

Updated Dec 27, 2023
Jupyter Notebook

Aashir01124 / Neural-Network-Modifications-Hyperparameter-Experiments

Modifies a neural network's hyperparameters, activation functions, cost functions, and regularization methods to improve training performance and generalization.

python deep-learning neural-network hyperparameter-optimization regularization epoch tanh leaky-relu sigmoid-function relu neural-network-training dropout-rates

Updated Aug 26, 2025

nafisa-samia / Implementing-Deep-Neural-Network-for-the-CIFAR-10-dataset

Deep Learning concepts practice using Cifar-10 dataset

deep-neural-networks deep-learning tensorflow keras leaky-relu elu-activation cifar-10 selu relu activation-functions cifar10-classification

Updated Jan 31, 2022
Jupyter Notebook

neural-tangjie / NTJ-DL-Activation_Functions_in_Neural_Network

This repository summarizes the basic concepts, types and usage scenarios of activation functions in deep learning.

deep-learning neural-network softmax leaky-relu sigmoid-function activation-functions relu-activation tanh-activation binary-step-function linear-activation-function non-linear-activation-function

Updated Aug 12, 2022

kuntala-c / Deep-Neural-Network-using-Cifar-10-dataset

Deep Learning concepts practice using Cifar-10 dataset

deep-neural-networks deep-learning tensorflow keras leaky-relu elu-activation cifar-10 selu relu cifar10-classification activation-function

Updated Feb 1, 2022
Jupyter Notebook

anishchapagain / imdb_deeplearning

Sentiment Classifier using Neural Networks & PyTorch.

neural-networks sentiment-classification leaky-relu imdb-dataset pytorch-implementation

Updated Aug 16, 2025
Python

MOSTAFA1172m / Phishing-detection-using-Neural-Networks

This project implements a machine learning model to detect phishing websites using a Multi-Layer Perceptron (MLP) neural network. The model analyzes various features extracted from URLs and website characteristics to classify them as either legitimate or phishing. The dataset used is dataset_phishing.csv, which contains 87 features and a binary lab

machine-learning pytorch cybersecurity neural-networks classification data-preprocessing model-evaluation leaky-relu relu phishing-detection accuracy-metrics dropout-layers binary-cross-entropy-loss

Updated Apr 30, 2025
Jupyter Notebook

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