dropout-layers
There are 38 repositories under dropout-layers topic.
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
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
MoinDalvs/Neural_Networks_Forest_Fire_Classification
PREDICT THE BURNED AREA OF FOREST FIRES WITH NEURAL NETWORKS
MoinDalvs/Neural_Network_Regression_Gas_Turbines
Predicting Turbine Energy Yield (TEY) using ambient variables as features.
rachelsohzc/Simple-stock-prediction-with-ANN
Predicting Meta stock prices using MLP, RNN and LSTM models.
dcarpintero/nn-image-classifier
Python from-scratch implementation of a Neural Network Classifier. Dive into the fundamentals of approximation, non-linearity, regularization, gradients, and backpropagation.
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.
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.
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.
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.
ADITYASHAH-IITP/Measure-of-Diabetes-progression
A quantitative measure of disease progression one year after baseline
dcarpintero/transformer101
Annotated vanilla implementation in PyTorch of the Transformer model introduced in 'Attention Is All You Need'.
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
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.
jungsoh/gru-trigger-word-detection
Recurrent neural network with GRUs for trigger word detection from an audio clip
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)
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.
Bless1004/Deep-Learning
Deep Learning models
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
Hsnmhmd/Vision
in this repo, you will find implementation of various classification models, data augmantation ,cnn designing and model reguralization
josericodata/MScDataAnalyticsSecondSemesterAssignmentOne
Summary of Assignment One from the Second semester of the MSc in Data Analytics program. This repository contains the CA1 assignment guidelines from the college and my submission. To see all original commits and progress, please visit the original repository using the link below.
LorenzoCastiglia/Deep-Learning-for-Image-Classifiaction
Deep Learning project about the design and training of a model for Image Classification
pyserve/Digit_Recognition_CNN
Model Optimization using Batch Normalization and Dropout Techniques
ritikdhame/Stockprice_prediction_LSTM
This project aims to build an Multivariate time series prediction LSTM model to predict the stock price.
SannketNikam/Churn-Modelling
This repository provides a simple implementation of churn prediction using Artificial Neural Networks for beginners in deep learning.
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.
siddharthiyervarma/CNN-MNIST
The aim was to develop a robust Convolutional Neural Network (CNN) for accurately classifying handwritten digits from the MNIST dataset
Stiti-create/CNN-Implementation-Numpy
Implementation of CNN (consisting of maxpool, relu, fully-connected and convolutional layers) using Numpy Vectorisation (from scratch without any third-party library), followed by analysis using hyperparameter tuning and different regularisation techniques
vicaaa12/Deep-Neural-Networks
Neural Network
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.
Flavio-Mangione/Spectrogram-images-with-neural-networks
Through the AlexNet and VGG16 convolutional networks, the neural networks were trained on a set of 600 spatial spectrogram images divided into 3 categories and divided into train, test, validation test
MK-ek11/Implement-GAN-on-MNIST-Dataset
Implement GAN (Generative Adversarial Network) on MNIST dataset. Vary the hyperparameters and analyze the corresponding results.
raufsingh/DL-Projects
There are various projects related to Neural network, computer vision and image processing.
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.
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
yaofuzhou/tesseract
[Work in Progress] Forked for Dropout Mechanism Development