max-pooling

ahmedfgad/CIFAR10CNNFlask

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

mayur7garg/PlantLeafDiseaseDetection

Deep learning using CNN in tensorflow on Kaggle image dataset containing 87,900 different healthy and unhealthy crop leaves spanning 38 unique classes.

aNdr3W03/Image-Classification-Deployment

Machine Learning For Beginners - Image Classification Model Deployment

RimTouny/Credit-Card-Fraud-Detection

Focused on advancing credit card fraud detection, this project employs machine learning algorithms, including neural networks and decision trees, to enhance fraud prevention in the banking sector. It serves as the final project for a Data Science course at the University of Ottawa in 2023.

danielmuthama/ML-Classifier-dogsvscats-Dataset

Ensemble Classifier

harinik05/LeukoDif

Denoising Diffusion Medical Model (DDMM) on PyTorch for generating datasets of Acute Lymphoblastic Leukemia 🩺💜

minecraftdixit/Digital-ASIC-LAB

Verilog Codes for various Design

TrainingByPackt/Applied-Deep-Learning-with-Keras-eLearning

Solve complex real-life problems with the simplicity of Keras

aNdr3W03/Rock-Paper-Scissors-Classification

Machine Learning For Beginners - Rock, Paper, dan Scissors Image Classification

cnavneet/DIGICAM

Digitally recognizing numbers in real life images has been a tough problem in artificial intelligence for many decades. The problem stems from the seemingly endless variations on fonts, colors, spacings, locations etc that these numbers can take within an image.

csbanon/mnist-classifiers

A collection of Jupyter notebooks containing various MNIST digit and fashion item classification implementations using fully-connected and convolutional neural networks (CNNs) built with TensorFlow and Keras. 2020.

Grace-Hephzibah/Automobile-Parts-Classifier

A CNN Architecture classifies 14 kinds of automobile parts.

Mayur-Kyatham/SignoSpeak

American Sign Language (ASL) Detection using CNN

nazli-d/Binary-Classification-Using-CNN

This project utilizes a CNN model to classify cat and dog images through training and testing processes. The model is created using the Keras library on the TensorFlow backend.

AleksandraAleksandrova/CS50-AI-P5-traffic

Project for lecture 5 Neural Networks to "Artificial Intelligence with Python" Harvard course

ananyaroy1011/Image-Segmentation

Deep Convolutional Encoder-Decoder Architecture implemented along with max-pooling indices for pixel-wise semantic segmentation using CamVid dataset.

applesoju/DeepNeuralNetworks-P

chandnii7/Image-Segmentation

Deep Convolutional Encoder-Decoder Architecture implemented along with max-pooling indices for pixel-wise semantic segmentation using CamVid dataset.

lokeshraosaab/Facial_Emotion_Recognition

Facial Emotion detection involves analysis of images or videos of faces to identify emotions based on the facial expressions

peter-strom/ML-p3-CNN

RobertRusev/NLP-FinHeadlines-MoodTracker

NLP-FinHeadlines-MoodTracker is a NLP project utilising sentiment analysis on financial news headlines. It employs a combination of CNN and LSTM layers to predict sentiment (positive, negative, neutral). The model incorporates an embedding layer, 1D convolution, max pooling, bidirectional LSTM, dropout, and dense layer for sentiment classification.

RobinMeneust/AI

AI model from scratch in C++ for image classification (MNIST dataset)

SamaSamrin/Basic-CNN-Implementation

A beginner-level implementation of the Convolutional Neural Network or CNN, which is an essential algorithm in image processing.

AkashSDas/how-does-convolutions-work

Visualizing effects of CNN filters and Max Pooling on images.

narayanacharya6/SBU-NLP-HW4

Relationship Extraction using a Bi-directional GRU v/s CNN with multiple layers and max-pooling

Pankaja-Suganda/Net-Engine-IP-With-Software

Net Engine FPGA with Software is an FPGA accelerator that enhances CNN performance in embedded systems by offloading tasks like 2D convolution and max-pooling, featuring the complete design of the Net Engine IP, software drivers, pre-trained models, and test data for facial computing.