Image classifier application to classify flowers to 102 categories, using TnensorFlow hub and Conv2D

Handwriting digit recognition using keras.Conv2D and MNIST database.

Creating a classifier for the German Traffic Signs dataset that classifies images of traffic signs into 43 classes.

This project is to apply Convolutional Neural Networks (CNN) to recognize dog breeds.

This repository Investigates DCGAN using facedata. Serves as a personal cautionary tale when working with GANS.

🐱 A deep learning model using CNN to classify between cat and dog images

Keras Convolutianl Networks

This model helps us classify 10 different real-life objects by undergoing training under tensorflow's CIFAR dataset which contains 60,000 32x32 color images with 6000 images of each class. I have made use of a stack of Conv2D and MaxPooling2D layers followed by a few densely connected layers.

In this project, used TensorFlow to build a neural network to classify road signs based on an image of those signs.

CIFAR10 Dataset.

A Simple Trained LeNET Model for handwritten digit recognition

Image Classification with 2D Convolutions, Deeplearning

Utilized CNN models to classify images of mountains and forests, treating mountains as the positive class and forests as the negative class. We compare the performance of a pre-trained model, a custom CNN model, and a CNN model with data augmentation.

This repository provides topics in PyTorch which is used for Deep Learning

This project explores the application of advanced neural network architectures, including Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), to forecast traffic volume. 👍👍✅

Image Augmentation

MNIST is the de facto “hello world” dataset of computer vision. In this competition, our goal is to correctly identify digits from a dataset of handwritten images.