Relationship Extraction using a Bi-directional GRU v/s CNN with multiple layers and max-pooling
-
Updated
Dec 30, 2019 - Python
Relationship Extraction using a Bi-directional GRU v/s CNN with multiple layers and max-pooling
Deep Convolutional Encoder-Decoder Architecture implemented along with max-pooling indices for pixel-wise semantic segmentation using CamVid dataset.
A beginner-level implementation of the Convolutional Neural Network or CNN, which is an essential algorithm in image processing.
Project for lecture 5 Neural Networks to "Artificial Intelligence with Python" Harvard course
Deep Convolutional Encoder-Decoder Architecture implemented along with max-pooling indices for pixel-wise semantic segmentation using CamVid dataset.
Visualizing effects of CNN filters and Max Pooling on images.
AI model from scratch in C++ for image classification (MNIST dataset)
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.
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.
Facial Emotion detection involves analysis of images or videos of faces to identify emotions based on the facial expressions
Machine Learning For Beginners - Rock, Paper, dan Scissors Image Classification
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.
A CNN Architecture classifies 14 kinds of automobile parts.
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.
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.
American Sign Language (ASL) Detection using CNN
Verilog Codes for various Design
Ensemble Classifier
Add a description, image, and links to the max-pooling topic page so that developers can more easily learn about it.
To associate your repository with the max-pooling topic, visit your repo's landing page and select "manage topics."