dropout-layers

There are 0 repository under dropout-layers topic.

VivianoRiccardo / Learning-Lab-C-Library
This library provides a set of basic functions for different type of deep learning (and other) algorithms in C.This deep learning library will be constantly updated
deeplearning neural-network c library backpropagation softmax convolutional-layers residual-networks residual-layers nesterov adam-algorithm adam-optimizer avarage opencl optimization-algorithms transformer dropout-layers deep-learning lstm
Language:C 32
BabyGPT-Build_GPT_From_Scratch
TatevKaren / BabyGPT-Build_GPT_From_Scratch
BabyGPT: Build Your Own GPT Large Language Model from Scratch Pre-Training Generative Transformer Models: Building GPT from Scratch with a Step-by-Step Guide to Generative AI in PyTorch and Python
attention-is-all-you-need dropout-layers gpt gpt-3 large-language-models layer-normalization llm-training llms multi-head-self-attention neural-networks python pytorch residual-connections transformers
Language:Python 30
MoinDalvs / Neural_Networks_Forest_Fire_Classification
PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS
auc-roc-curve binary-classification classification dropout-layers epochs hyperparameter-optimization hyperparameter-tuning keras-classification-models neural-networks tensorflow weight-initialization
Language:Jupyter Notebook 6
MoinDalvs / Neural_Network_Regression_Gas_Turbines
Predicting Turbine Energy Yield (TEY) using ambient variables as features.
activation-functions dropout-keras dropout-layers hyperparameter-optimization hyperparameter-tuning keras-neural-networks neural-networks neurons regression weights-and-biases
Language:Jupyter Notebook 4
rachelsohzc / Simple-stock-prediction-with-ANN
Predicting Meta stock prices using MLP, RNN and LSTM models.
neural-networks lstm recurrent-neural-networks deep-neural-networks stock-price-prediction mlp dropout-layers sliding-windows
Language:Jupyter Notebook 4
Sanky18 / L-T-EduTech-Hackathon-at-SHAASTRA-IITM
Concrete cracking is a major issue in Bridge Engineering. Detection of cracks facilitates the design, construction and maintenance of bridges effectively.
binaryclassification cnn deep-neural-networks inception-v3 keras-tensorflow machine-learning modeling tensorflow2 transferlearning agumentation dropout-layers regularization
Language:Jupyter Notebook 3
Abdulrahmankhaled11 / Customer-Churn-Using-Deep-Learning
ANN model to predict customer churn based on some information about the customer and used Dropout regulization to avoid overfitting in my model.
ann deep-learning deep-neural-networks dropout-layers encoding machine-learning
Language:Jupyter Notebook 2
copev313 / Deep-Learning-Introduction
A collection of deep learning exercises collected while completing an Intro to Deep Learning course. We use TensorFlow and Keras to build and train neural networks for structured data.
python jupyter-notebook deep-learning neural-networks tensorflow keras linear linear-unit tpu-acceleration batch-normalization dropout-layers gradient-descent binary-classification higgs-boson
Language:Jupyter Notebook 2
dcarpintero / nn-image-classifier
Python from-scratch implementation of a Neural Network Classifier
backpropagation deep-learning dropout-layers gradient-descent linear-layers multilayer-perceptron python relu-activation fashion-mnist image-classification
Language:Jupyter Notebook 2
Sanky18 / Crack-Detection-Model
Concrete cracking is a major issue in Bridge Engineering. Detection of cracks facilitates the design, construction and maintenance of bridges effectively.
augmentation binaryclassification cnn deepneuralnetworks dropout-layers inception-v3 keras-tensorflow machine-learning modeling regularization transfer-learning
Language:Jupyter Notebook 2
ADITYASHAH-IITP / Measure-of-Diabetes-progression
A quantitative measure of disease progression one year after baseline
diabetes-prediction quantitative-measure deep-neural-networks regularization-methods early-stopping dropout-layers
Language:Jupyter Notebook 1
dcarpintero / transformer101
Annotated vanilla implementation in PyTorch of the Transformer model introduced in 'Attention Is All You Need'
attention-is-all-you-need encoder-decoder-architecture multihead-attention positional-encoding self-attention transfomer dot-product-attention dropout-layers feedforward-neural-network gelu linear-layers normalization-layers softmax pytorch
Language:Jupyter Notebook 1
hrootscraft / asl-translator
Translates the live video feed from opencv into text format and displays this onto the frame. Uses LSTM, Dropouts, Regularizers and Learning Rate Scheduler
bayesian-optimization deep-learning dropout-layers keras-tensorflow lstm mediapipe multiclass-classification opencv-python
Language:Jupyter Notebook 1
jaunel / CNN-Image-Classifier
A Image classification CNN model with more than 85% accuracy. An interactive API is been designed using flask framework for better user experience. Techniques like batch normalization, dropouts is used for improved accuracy.
batch-normalization bootstrap cnn-keras deep-learning dropout-layers flask-application html jinja-template machine-learning-algorithms tenserflow
Language:Jupyter Notebook 1
jungsoh / gru-trigger-word-detection
Recurrent neural network with GRUs for trigger word detection from an audio clip
batch-normalization convolutional-layers dropout-layers gated-recurrent-unit gru recurrent-neural-network rnn-gru speech-recognition trigger-word-detection
Language:Jupyter Notebook 1
ryanquinnnelson / CMU-11685-Frame-Level-Classification-of-Speech-using-Deep-Learning
Fall 2021 Introduction to Deep Learning - Homework 1 Part 2 (Frame Level Classification of Speech)
mlp deep-learning torch multilayer-perceptron batch-normalization dropout-layers phonemes melspectrogram-classifications pytorch kaggle-competition wandb octopus
Language:Python 1
somjit101 / MNIST-Classification-Tensorflow
A simple study on how to use Tensorflow platform (without Keras) for a simple number classification task using a Neural Network.
deep-learning neural-network tensorflow dropout-layers batch-normalization mnist-classification mnist-dataset
Language:Jupyter Notebook 1
Bless1004 / Deep-Learning
Deep Learning models
batchnormalization dropout-layers classification imbalanced-data neural-network
Language:Jupyter Notebook 0
HealthyData-Hub / Predicting-the-Early-Stages-of-Alzheimers-Disease-Part-1
To provide a complete pipeline to develop a deep learning model. More specifically, a multiclass classification (single label) deep learning model that can predict what stage of Alzheimer's a patient is, from their MRI image
alzheimers-disease datascience deeplearning dementia-diagnosis imageclassification medical-imaging neural-networks smote tensorflow sequential-models dropout-layers
Language:Jupyter Notebook 0
Hsnmhmd / Vision
in this repo, you will find implementation of various classification models, data augmantation ,cnn designing and model reguralization
batch-normalization cnn data-augmentation dnn dropout-layers image-classification mlp mlp-classifier model-tuning resnet-101 transfer-learning
Language:Jupyter Notebook 0
LorenzoCastiglia / Deep-Learning-for-Image-Classifiaction
Deep Learning project about the design and training of a model for Image Classification
bias-variance-tradeoff cnn-classification deeplearning dropout-layers fine-tuning keras-tensorflow numpy pandas regularization-hyperparameters scikit-learn transfer-learning
Language:Jupyter Notebook 0
pyserve / Digit_Recognition_CNN
Model Optimization using Batch Normalization and Dropout Techniques
batchnormalization cnn digitrecognition dropout-layers model-optimization
Language:Jupyter Notebook 0
ritikdhame / Stockprice_prediction_LSTM
This project aims to build an Multivariate time series prediction LSTM model to predict the stock price.
dropout-layers keras-tensorflow lstm plotly stock-price-prediction time-series-analysis
Language:Jupyter Notebook 0
SannketNikam / Churn-Modelling
This repository provides a simple implementation of churn prediction using Artificial Neural Networks for beginners in deep learning.
ann churn-prediction deep-learning dropout-layers hyperparameter-tuning keras neural-networks tensorflow
Language:Jupyter Notebook 0
vicaaa12 / Deep-Neural-Networks
Neural Network
artificial-neural-networks auc-roc-curve batch-normalization callback crossvalidation dropout-layers earlystopping hyperparameter-tuning k-fold-cross-validation keras-classification-models keras-tensorflow multi-layer-perceptron regularization sequential-models weight-initialization weights-and-biases
Language:Jupyter Notebook 0
akash18tripathi / Exploring-MultiLayer-Perceptrons
This GitHub repository explores the importance of MLP components using the MNIST dataset. Techniques like Dropout, Batch Normalization, and optimization algorithms are experimented with to improve MLP performance. Gain a deeper understanding of MLP components and learn to fine-tune for optimal classification performance on MNIST.
batch-normalization dropout-layers loss-functions mini-batch-gradient-descent mnist-dataset multilayer-perceptron-network optimization-algorithms pytorch stochastic-gradient-descent
Language:Jupyter Notebook
hmcalister / MNIST-Machine-Learning-Investigation
A beginner's investigation into the world of neural networks, using the MNIST image dataset
model-architectures dense-layers dropout-rates convolutional-layers dropout-layers
Language:Python
MK-ek11 / Implement-GAN-on-MNIST-Dataset
Implement GAN (Generative Adversarial Network) on MNIST dataset. Vary the hyperparameters and analyze the corresponding results.
batchnorm deep-neural-networks dropout-layers fnn fully-connected-neural-network generative-adversarial-network matplotlib neural-network numpy pytorch
Language:Jupyter Notebook
Shreejan-git / neural_network_scratch
In this repository I have included all the ipynb files in which I have tried to implement the neural network and other concepts from scratch.
dropout-layers neural-network regularization
Language:Jupyter Notebook
siddharthiyervarma / -DeepSonar_Classifier-
The primary objective of this project is to design and train a deep neural network that can generalize well to new, unseen data, effectively distinguishing between rocks and metal cylinders based on the sonar chirp returns.
deep-neural-networks dropout-layers l2-regularization neural-network overfitting-reduced
Language:Jupyter Notebook
siddharthiyervarma / CNN-MNIST
The aim was to develop a robust Convolutional Neural Network (CNN) for accurately classifying handwritten digits from the MNIST dataset
cnn-keras dropout-layers image-classification l2-regularization neural-network
Language:Jupyter Notebook
somjit101 / DCGAN-Gradient-Tape
A study of the use of the Tensorflow GradientTape class for differentiation and custom gradient generation along with its use to implement a Deep-Convolutional Generative Adversarial Network (GAN) to generate images of hand-written digits.
gradient-tape tensorflow keras-tensorflow neural-network gan generative-adversarial-network dcgan-tensorflow convolutional-layers deconvolution transpose-convolution batch-normalization dropout-layers mnist-dataset
Language:Jupyter Notebook
vaibhavdangar09 / Stock_Market_Prediction_And_Forecasting_Using_Bidirectional_LSTM_RNN
Utilizing advanced Bidirectional LSTM RNN technology, our project focuses on accurately predicting stock market trends. By analyzing historical data, our system learns intricate patterns to provide insightful forecasts. Investors gain a robust tool for informed decision-making in dynamic market conditions. With a streamlined interface, our solution
bidirectional-lstm data data-science deep-learning deep-neural-networks dropout dropout-layers early-stopping forecasting machine-learning machine-learning-algorithms python python3 r2-score rnn stock-price-prediction
Language:Jupyter Notebook

dropout-layers

VivianoRiccardo / Learning-Lab-C-Library

TatevKaren / BabyGPT-Build_GPT_From_Scratch

MoinDalvs / Neural_Networks_Forest_Fire_Classification

MoinDalvs / Neural_Network_Regression_Gas_Turbines

rachelsohzc / Simple-stock-prediction-with-ANN

Sanky18 / L-T-EduTech-Hackathon-at-SHAASTRA-IITM

Abdulrahmankhaled11 / Customer-Churn-Using-Deep-Learning

copev313 / Deep-Learning-Introduction

dcarpintero / nn-image-classifier

Sanky18 / Crack-Detection-Model

ADITYASHAH-IITP / Measure-of-Diabetes-progression

dcarpintero / transformer101

hrootscraft / asl-translator

jaunel / CNN-Image-Classifier

jungsoh / gru-trigger-word-detection

ryanquinnnelson / CMU-11685-Frame-Level-Classification-of-Speech-using-Deep-Learning

somjit101 / MNIST-Classification-Tensorflow

Bless1004 / Deep-Learning

HealthyData-Hub / Predicting-the-Early-Stages-of-Alzheimers-Disease-Part-1

Hsnmhmd / Vision

LorenzoCastiglia / Deep-Learning-for-Image-Classifiaction

pyserve / Digit_Recognition_CNN

ritikdhame / Stockprice_prediction_LSTM

SannketNikam / Churn-Modelling

vicaaa12 / Deep-Neural-Networks

akash18tripathi / Exploring-MultiLayer-Perceptrons

hmcalister / MNIST-Machine-Learning-Investigation

MK-ek11 / Implement-GAN-on-MNIST-Dataset

Shreejan-git / neural_network_scratch

siddharthiyervarma / -DeepSonar_Classifier-

siddharthiyervarma / CNN-MNIST

somjit101 / DCGAN-Gradient-Tape

vaibhavdangar09 / Stock_Market_Prediction_And_Forecasting_Using_Bidirectional_LSTM_RNN