This project is to apply Convolutional Neural Networks (CNN) to recognize dog breeds.
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Updated
Mar 4, 2018 - HTML
This project is to apply Convolutional Neural Networks (CNN) to recognize dog breeds.
Image Classification with 2D Convolutions, Deeplearning
A Simple Trained LeNET Model for handwritten digit recognition
Handwriting digit recognition using keras.Conv2D and MNIST database.
Kaggle Machine Learning Competition Project : In this project, we will create a classifier to classify fashion clothing into 10 categories learned from Fashion MNIST dataset of Zalando's article images
A repository for machine learning problems and exploration of different ML libraries. The goal of this repository is to collect takeaways while developing ML models. This should improve my overall understanding of developing machine learning applications.
Benchmarks across Deep Learning Frameworks in Julia and Python
🐱 A deep learning model using CNN to classify between cat and dog images
Reinforcement Learning with Actor-Critic to play Breakout-v4 (Atari) from OpenAI Gym
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.
Image classifier application to classify flowers to 102 categories, using TnensorFlow hub and Conv2D
2D Convolutional Recurrent Neural Networks implemented in PyTorch
Mokka is a minimal Inference Engine for Dense and Convolutional 2D Layer Neural Networks. Written on a single C++ header, it uses AVX2
Deployed the super-resolution convolution neural network (SRCNN) using Keras. Recovers a high-resolution image from a low-resolution input.
Convolutional Neural Network to Classify Dogs and Cat. I built a ImageClassifier which classifies and tells you whether its a Dog image or a Cat image. I built a convolutional network which consists of Three Convolution layer and Three MaxPooling layer. Each Convolutional layer has filters, kernel size. Maxpooling layer has stride and pooling si…
Image classification based computer vision model CNN
In this project, we will create a classifier to classify fashion clothing into 10 categories learned from Fashion MNIST dataset.
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.
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