elaheghiyabi96/fashion_mnist_nn_torch

"Simple neural network model using Torch for classifying the Fashion MNIST dataset, implemented with Torch."

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Pizzacus/satania.moe

Satania IS the BEST waifu, no really, she is, if you don't believe me, this website will convince you

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iscience-kn/dropR

drop out analysis with R and shiny

Language: R - Size: 8.35 MB - Last synced at: 13 days ago - Pushed at: 13 days ago - Stars: 6 - Forks: 0

mimihime0/CNN-Fashion-MNIST-Classifier

A convolutional neural network (CNN) for classifying the Fashion-MNIST dataset. Includes experiments with regularization techniques, data augmentation, and hyperparameter tuning to optimize model performance, achieving 89.76% test accuracy.

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MorvanZhou/Tensorflow-Tutorial

Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学

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VITA-Group/Random-MoE-as-Dropout

[ICLR 2023] "Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers" by Tianlong Chen*, Zhenyu Zhang*, Ajay Jaiswal, Shiwei Liu, Zhangyang Wang

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MorvanZhou/PyTorch-Tutorial

Build your neural network easy and fast, 莫烦Python中文教学

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VITA-Group/Deep_GCN_Benchmarking

[TPAMI 2022] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

Language: Python - Size: 805 KB - Last synced at: 21 days ago - Pushed at: over 3 years ago - Stars: 125 - Forks: 21

ivannz/cplxmodule

Complex-valued neural networks for pytorch and Variational Dropout for real and complex layers.

Language: Python - Size: 473 KB - Last synced at: 16 days ago - Pushed at: almost 3 years ago - Stars: 142 - Forks: 28

zmyzheng/Neural-Networks-and-Deep-Learning

Deep learning projects including applications (face recognition, neural style transfer, autonomous driving, sign language reading, music generation, translation, speech recognition and NLP) and theories (CNNs, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, hyperparameter tuning, regularization, optimization, Residual Networks). Deep Learning Specialization by Andrew Ng, deeplearning.ai

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AnicetNgrt/jiro-nn

A Deep Learning and preprocessing framework in Rust with support for CPU and GPU.

Language: Rust - Size: 17.5 MB - Last synced at: 10 days ago - Pushed at: over 1 year ago - Stars: 130 - Forks: 3

vickshan001/CIFAR-10-CNN-Enhancer-Neural-Networks

CNN classifier for CIFAR-10 with enhanced architecture, dropout, and data augmentation.

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masoudshahrian/DeepLearning-Code

Deep learning Projects with code

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rezakj/iCellR

Single (i) Cell R package (iCellR) is an interactive R package to work with high-throughput single cell sequencing technologies (i.e scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST)).

Language: R - Size: 68.2 MB - Last synced at: about 5 hours ago - Pushed at: 10 months ago - Stars: 122 - Forks: 19

seba-1511/lstms.pth

PyTorch implementations of LSTM Variants (Dropout + Layer Norm)

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JonathanRaiman/theano_lstm

:microscope: Nano size Theano LSTM module

Language: Python - Size: 91.8 KB - Last synced at: about 11 hours ago - Pushed at: over 8 years ago - Stars: 303 - Forks: 112

thtrieu/essence

AutoDiff DAG constructor, built on numpy and Cython. A Neural Turing Machine and DeepQ agent run on it. Clean code for educational purpose.

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Jackpopc/aiLearnNotes

Artificial Intelligence Learning Notes.

Language: Python - Size: 638 KB - Last synced at: about 1 month ago - Pushed at: about 2 years ago - Stars: 273 - Forks: 60

SedCore/FTDropBlock

Features-Time DropBlock (FT-DropBlock) regularization strategy for EEG-based CNNs.

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RabadanLab/randomly

A Library for Denoising Single-Cell Data with Random Matrix Theory

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RizwanMunawar/Cats-vs-dogs-classification-computer-vision-

Cats vs dogs classification using deep learning. Data augmentation and convolutional neural networks.

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pngo1997/Fashion-MNIST-Classification-with-TensorFlow-Keras

Explores image classification using a Multi-layer Feed-Forward Neural Network on the Fashion MNIST dataset.

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aurelio-amerio/ConcreteDropout

Concrete Dropout implementation for Tensorflow 2.0 and PyTorch

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Cohere-Labs-Community/Targeted-Dropout

Complementary code for the Targeted Dropout paper

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mohamedkhayat/DIYNeuralNet

This repository contains a multi-layer neural network implemented from scratch using NumPy. It supports forward and backward propagation, dropout regularization, and flexible architecture definition, making it a versatile tool for training deep neural networks. The project focuses on stability with proper initialization and scaling techniques.

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EPSOFT/Keras-Convolutianl-Networks

Keras Convolutianl Networks

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sushant1827/LSTM_for_Household_Power_Consumption

This project explores the application of Long Short-Term Memory (LSTM) networks in predicting household power consumption. Using data collected at one-minute intervals, we demonstrate how LSTM can be leveraged for accurate forecasting.

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sushant1827/Fashion-Clothing-Classification

Kaggle Machine Learning Competition Project : In this project, we will create a classifier to classify fashion clothing into 10 categories learned from Fashion MNIST dataset of Zalando's article images

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rakibhhridoy/AnomalyDetectionInTimeSeriesData-Keras

Statistics, signal processing, finance, econometrics, manufacturing, networking[disambiguation needed] and data mining, anomaly detection (also outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions.

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ahmedfgad/CIFAR10CNNFlask

Building a HTTP-accessed convolutional neural network model using TensorFlow NN (tf.nn), CIFAR10 dataset, Python and Flask.

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hwalsuklee/tensorflow-mnist-cnn

MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.

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Ahmed-hassan-AI/nlp-Sentiment-Analysis

Sentiment Analysis

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parasdahal/deepnet

Educational deep learning library in plain Numpy.

Language: Python - Size: 40 KB - Last synced at: 6 months ago - Pushed at: almost 3 years ago - Stars: 322 - Forks: 83

PRIS-CV/AdvancedDropout

Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization (IEEE TPAMI 2021)

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arkanivasarkar/Deep-Learning-from-Scratch

Implementation of a Fully Connected Neural Network, Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) from Scratch, using NumPy.

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mosswg/dropout-dl

A tool for downloading dropout.tv episodes

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kalthommusa/Udacity-Intro-to-Deep-Learning-Introduction-to-Neural-Network

Collection of my notes from Udacity's Intro to Deep Learning--> Introduction to Neural Networks course.

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gianlucatruda/Spo2_evaluation Fork of CoVital-Project/Spo2_evaluation

Covid-19 | Quantifying Uncertainty in Blood Oxygen Estimation Models from Real-World Data

Language: Python - Size: 332 MB - Last synced at: 5 months ago - Pushed at: over 4 years ago - Stars: 3 - Forks: 0

krishcy25/TimeSeriesModeling-Apple-Stock-Prediction

This repository focuses on building Time Series Model (Recurrent Neural Network- LSTM) to predict the stock price of the Apple.Long Short-Term Memory (LSTM) networks are a type of recurrent neural network capable of learning order dependence in sequence prediction problems that involves time series related events

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j-min/Dropouts

PyTorch Implementations of Dropout Variants

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lauracarpaciu/Bees-vs-Wasps

Distinguish bees from wasps

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harmanveer-2546/Retinal-Disease-Classification

The number of visually impaired people worldwide is estimated to be 2.2 billion, of whom at least 1 billion have a vision impairment that could have been prevented or is yet to be addressed. Early detection and diagnosis of ocular pathologies would enable forestall of visual impairment.

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VikentiosVitalis/image_and_video_analysis_and_technology

Laboratories - for 'Image and Video Analysis and Technology' M.Sc. Course ECE @ntua

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ishreya09/Skin-Cancer-Detection

Developed a CNN model to classify skin moles as benign or malignant using a balanced dataset from Kaggle, achieving a test accuracy of 81.82% and an AUC of 89.06%. Implemented data preprocessing by resizing images to 224x224 pixels and normalizing pixel values, enhancing model performance and stability.

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beenish-Ishtiaq/DEP-Task-4-Image-Classification-Cifar10

Developed a Convolutional Neural Network (CNN) model to classify images into 10 categories. The project includes data augmentation, model building, training, and evaluation.

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RajK01/Google-Customer-Revenue-Prediction

The main aim of this project is to built a predictive model using G Store data to predict the TOTAL REVENUE per customer that helps in better use of marketing budget.

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lonePatient/daguan_2019_rank9

datagrand 2019 information extraction competition rank9

Language: Python - Size: 4.2 MB - Last synced at: about 1 month ago - Pushed at: over 5 years ago - Stars: 130 - Forks: 43

HarikrishnanK9/Tomato_Leaf_Disease_Detection

Tomato Leaf Disease Detection:Deep Learning Project

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harmanveer-2546/Diagnosis-Of-Pneumonia-By-CNN-Classifier

The primary objective s to develop an accurate and efficient classification model capable of identifying pneumonia cases in patients based on chest X-ray images. Pneumonia is a prevalent and potentially life-threatening respiratory infection. Early detection plays a critical role in timely intervention and effective treatment.

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emilwallner/Deep-Learning-101

The tools and syntax you need to code neural networks from day one.

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abeed04/Sentiment-Analysis-using-Recurrent-Neural-Networks

Bidirectional RNNs are used to analyze the sentiment (positive, negative, neutral) of movie reviews. .

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NowyTeam/Tempo

Language: TypeScript - Size: 9.26 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 1 - Forks: 0

chinmoyt03/Deep-Learning-Based-Diabetes-Risk-Analysis

Data Science Project: Comparing 3 Deep Learning Methods (CNN, LSTM, and Transfer Learning).

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CyberZHG/keras-targeted-dropout 📦

Targeted dropout implemented in Keras

Language: Python - Size: 15.6 KB - Last synced at: 4 months ago - Pushed at: almost 6 years ago - Stars: 6 - Forks: 4

AdalbertoCq/NeuralNetwork

Neural Network implementation in Numpy and Keras. Batch Normalization, Dropout, L2 Regularization and Optimizers

Language: Python - Size: 8.01 MB - Last synced at: 12 months ago - Pushed at: almost 6 years ago - Stars: 16 - Forks: 6

anassinator/bnn

Bayesian Neural Network in PyTorch

Language: Python - Size: 415 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 76 - Forks: 25

ThinamXx/NeuralNetworks_and_DeepLearning

In this repository, you will gain insights about Neural Networks and Deep Learning.

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miguelvr/dropblock

Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.

Language: Python - Size: 48.8 KB - Last synced at: about 1 year ago - Pushed at: almost 5 years ago - Stars: 581 - Forks: 95

shree-prada/Traffic-Signs-Recognition

This project is a real-time traffic sign recognition system built using Python, OpenCV, and a pre-trained CNN model, capable of detecting and recognizing traffic signs from images.

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thomastrg/DeepLearningPracticalWorks

Neural networks and deep learning practical works

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shouryasimha/Ships-In-Satellite-images

Satellite imagery provides unique insights into various markets, including agriculture, defense and intelligence, energy, and finance. New commercial imagery providers, such as Planet, are using constellations of small satellites to capture images of the entire Earth every day. This flood of new imagery is outgrowing the ability for organizations to manually look at each image that gets captured, and there is a need for machine learning and computer vision algorithms to help automate the analysis process. The aim is to help address the difficult task of detecting the location of large ships in satellite images. Automating this process can be applied to many issues including monitoring port activity levels and supply chain analysis.

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parkjjoe/snn-aware-dropout

Develop SNN-aware Noise Addition Layers

Language: Python - Size: 71.3 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

abhinavthapper31/waste-classification-CNN-Image-Augmentation

A model to classify images of waste products as Organic or Recyclable. Applied Image Augmentation to images and used basic CNN to classify images using Keras. Analysed the performance using Tensorboard. Detected over fitting using metric curves (accuracy) and addressed it using Dropout Regularization.

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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

Language: Jupyter Notebook - Size: 142 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

CyberZHG/keras-drop-block 📦

DropBlock implemented in Keras

Language: Python - Size: 13.7 KB - Last synced at: 20 days ago - Pushed at: over 3 years ago - Stars: 26 - Forks: 15

jeongwhanchoi/MLND-Capstone-Project

Capstone Project for Udacity Machine Learning Nanodegree

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manashpratim/Deep-Learning-From-Scratch

Language: Jupyter Notebook - Size: 1.53 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 0

applesoju/DeepNeuralNetworks-P

Language: Python - Size: 726 MB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 0 - Forks: 0

srinadhu/convolutional_nn

Implemented fully-connected DNN of arbitrary depth with Batch Norm and Dropout, three-layer ConvNet with Spatial Batch Norm in NumPy. The update rules used for training are SGD, SGD+Momentum, RMSProp and Adam. Implemented three block ResNet in PyTorch, with 10 epochs of training achieves 73.60% accuracy on test set.

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RimTouny/Weed-Species-Classification-and-Bounding-Box-Regression

Leveraging advanced image processing and deep learning, this project classifies plant images using a subset of the Plant Seedlings dataset. The dataset includes diverse plant species captured under varying conditions. This project holds significance within my Master's in Computer Vision at uOttawa (2023).

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Tasiabueno/Bayesian-Convolutional-Neural-Network-Crack-Detection

A Bayesian Convolutional Neural Network approach for image-based crack detection and maintenance applications

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MattMoony/convnet_mnist

Simple convolutional neural network (purely numpy) to classify the original MNIST dataset. My first project with a convnet. 🖼

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Honolulu69/Successful-Aging

Machine learning Algorithms for the Prediction of Successful Aging in Older Adults

Language: Python - Size: 103 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

DelTA-Lab-IITK/CD3A

Code for Curriculum based Dropout Discriminator for Domain Adaptation(CD3A), BMVC, 2019

Language: Lua - Size: 2.53 MB - Last synced at: 5 months ago - Pushed at: over 5 years ago - Stars: 9 - Forks: 4

sahildigikar15/Different-CNN-Architectures-on-MNIST-dataset-

Experimented with different architectures and kernels on MNIST dataset using Convolutional Neural Networks.

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sahildigikar15/MLP-Architetures-on-MNIST-dataset

Experimented with different architectures on MNIST dataset using MLPs with different dropouts.

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shimazadeh/Neural_Networks

the implementation of a multilayer perceptron

Language: Python - Size: 6.32 MB - Last synced at: 2 months ago - Pushed at: over 1 year ago - Stars: 0 - Forks: 0

the-lans/NeuroRepository

Фреймворк для построения нейронных сетей, комитетов, создания агентов с параллельными вычислениями.

Language: C++ - Size: 72.3 MB - Last synced at: about 1 year ago - Pushed at: over 4 years ago - Stars: 11 - Forks: 8

sharmaroshan/Weed-Detection

This Problem is based on a Image Data set consisting of different types of weeds, to detect them in crops and fields. I have used Deep Learning Model called CNN(Convolutional Neural Networks) with Dropout, Batch Normalization, ReduceLearning rate on plateau, Early stoppig rounds, and Transposd Convolutional Neural Networks.

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somefunAgba/deeplearningWithMatlabinPy

Investigating the Behaviour of Deep Neural Networks for Classification

Language: Python - Size: 1.15 MB - Last synced at: over 1 year ago - Pushed at: over 6 years ago - Stars: 1 - Forks: 0

danielkelshaw/ConcreteDropout

PyTorch implementation of 'Concrete Dropout'

Language: Python - Size: 399 KB - Last synced at: 5 months ago - Pushed at: over 1 year ago - Stars: 14 - Forks: 2

najeebkhan/sparseout

Sparseout: Controlling Sparsity in Deep Networks

Language: Python - Size: 5.86 KB - Last synced at: over 1 year ago - Pushed at: almost 6 years ago - Stars: 2 - Forks: 2

KlugerLab/ALRA

Imputation method for scRNA-seq based on low-rank approximation

Language: R - Size: 7.72 MB - Last synced at: over 1 year ago - Pushed at: almost 2 years ago - Stars: 63 - Forks: 18

devanshkhare1705/Personalizing-K12-Education

Using deep learning to predict whether students can correctly answer diagnostic questions

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deep-learning-algorithm/PyNet

Numpy implementation of deep learning

Language: Python - Size: 21.5 MB - Last synced at: over 1 year ago - Pushed at: almost 3 years ago - Stars: 13 - Forks: 4

georgezoto/TensorFlow-in-Practice

TensorFlow in Practice Specialization. Join our Deep Learning Adventures community 🎉 and become an expert in Deep Learning, TensorFlow, Computer Vision, Convolutional Neural Networks, Kaggle Challenges, Data Augmentation and Dropouts Transfer Learning, Multiclass Classifications and Overfitting and Natural Language Processing NLP as well as Time Series Forecasting 😀 All while having fun learning and participating in our Deep Learning Trivia games 🎉 http://bit.ly/deep-learning-tf

Language: Jupyter Notebook - Size: 124 MB - Last synced at: over 1 year ago - Pushed at: almost 5 years ago - Stars: 59 - Forks: 24

naoki-vn634/MCDropout

Implementation of Monte Carlo Dropout for Bayesian Convolutional Neural Network, Investigating Uncertainty of DeepNeuralNetwork

Language: Python - Size: 3.58 MB - Last synced at: over 1 year ago - Pushed at: about 4 years ago - Stars: 0 - Forks: 1

Palak-15/Satelite_Image_Processing

It is tensorflow 2.0 implementation on Eurosat Dataset. IT classfies different types of satelite images. Used transfer learning in end to reduce overfitting and increase accuracy.

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snrazavi/Machine-Learning-in-Python-Workshop

My workshop on machine learning using python language to implement different algorithms

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arjunsingh88/image_classification_cats_dogs

Image Classification problem, Cats v/s Dogs Model. Browse to https://imgclassification.herokuapp.com/ for the deployment via Heroku

Language: Jupyter Notebook - Size: 76.3 MB - Last synced at: about 1 year ago - Pushed at: about 2 years ago - Stars: 1 - Forks: 2

dendisuhubdy/fraternal-nmt

Neural Machine Translation with Fraternal Dropout

Language: Python - Size: 108 KB - Last synced at: 12 months ago - Pushed at: over 7 years ago - Stars: 5 - Forks: 1

CyberZHG/keras-drop-connect 📦

Drop-connect wrapper

Language: Python - Size: 12.7 KB - Last synced at: over 1 year ago - Pushed at: almost 4 years ago - Stars: 7 - Forks: 4

srinadhu/CS231n

My solutions for Assignments of CS231n: Convolutional Neural Networks for Visual Recognition

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Baksonator/fashionMNIST-classifier Fork of raf-bsn/ML-project2

Convoluted Neural Network for classifying the FashionMNIST data set. Recognition of multiple clothing objects on the same picture with noise using the trained model and OpenCV.

Language: Python - Size: 66.5 MB - Last synced at: over 1 year ago - Pushed at: over 5 years ago - Stars: 0 - Forks: 0

fraunhofer-iais/wasserstein-dropout

Wasserstein dropout (W-dropout) is a novel technique to quantify uncertainty in regression networks. It is fully non-parametric and yields accurate uncertainty estimates - even under data shifts.

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fraunhofer-iais/second-moment-loss

The second-moment loss (SML) is a novel training objective for dropout-based regression networks that yields improved uncertainty estimates.

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hugohiraoka/Bank_Customer_Churn_Prediction

Model to predict bank customer churn

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hwixley/MLP-coursework1-report

Machine Learning Practical - Coursework 1 Report: a study of the problem of overfitting in deep neural networks, how it can be detected, and prevented using the EMNIST dataset. This was done by performing experiments with depth and width, dropout, L1 & L2 regularization, and Maxout networks.

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hwixley/EMNIST-NeuralNet-Regularisation-Experiments

A study of the problem of overfitting in deep neural networks, how it can be detected, and prevented using the EMNIST dataset. This was done by performing experiments with depth and width, dropout, L1 & L2 regularization, and Maxout networks.

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khare19yash/CS231n

CS231n course assignment

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