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Revisions and implementations of modern Convolutional Neural Networks architectures in TensorFlow and Keras
A collection of some easy Neural Network based Python projects
Estimating galaxy gas mass fractions using SDSS imaging
PyTorch 1.x version's Tutorials using Google Colab: Overview, Regression, ConvNets, RNNs, GANs tutorials, etc.
Colorization of Image with Generative Adversarial Network (Pix2Pix).
Neural style art transfer made using a VGG-16 convolutional neural network using Keras and TensorFlow.
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 size. Then this Convolutional layer Connects to DeepNeuralNetwork. DNN has three hidden layer and output layer having Sigmoid Activation function. I trained this model for 31 epochs and achieved an accuracy of around 85%. I found this massive image dataset online which has 10,028 images(Ten Thousand and Twenty Eight). My model Predicted accurately during the testing phase. I even tested my model using my neighbor dog's pic and it predicted accurately.
Random Anime Face Generation with Generative Adversarial Networks. DCGAN and WGAN-GP are used.
Car Localization with Semantic Segmentation, using UNet. Dice Score: 0.89, Pixel to Pixel Accuracy: 99.07%.
Predicting Yaw and Pitch of a moving vehicle (comma ai) including more stuff that can help make your car drive itself.
Visualization and adversarial examples with a DIY (numpy only) ConvNet.
This is a Computer Vision Free Course from @Kaggle.
Techniques for interpreting ConvNets