Relationship Extraction using a Bi-directional GRU v/s CNN with multiple layers and max-pooling
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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.
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.
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)
Denoising Diffusion Medical Model (DDMM) on PyTorch for generating datasets of Acute Lymphoblastic Leukemia 🩺💜
A beginner-level implementation of the Convolutional Neural Network or CNN, which is an essential algorithm in image processing.
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.
Machine Learning For Beginners - Image Classification Model Deployment
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.
Facial Emotion detection involves analysis of images or videos of faces to identify emotions based on the facial expressions
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.
American Sign Language (ASL) Detection using CNN
Ensemble Classifier
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.
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