Displaying 1 to 9 from 9 results

This is the code for the article 'Turning design mockups into code with deep learning' on FloydHub's blog. Within three years deep learning will change front-end development. It will increase prototyping speed and lower the barrier for building software.

keras deep-learning seq2seq encoder-decoder lstm floydhub machine-learning cnn cnn-keras jupyter-notebook jupyterTo make a lane follower based on a standard RC car using Raspberry Pi and a camera. The software is a simple Convolutional Network, which takes in the image fetched from the camera and outputs the steering angle. During data collection, we will simply hook the steering PWM of the car to pin GPIO17. The script raspberry_pi/collect_data.py will record the values of steering PWM and the associated images. The data of each trial are collectively stored in driving_trial_*. The trial folders are automatically numbered.

raspberry-pi cnn-keras deep-learning machine-learning servo self-driving-car convolutional-networksThis is an implementation in Keras of the paper "3D Bounding Box Estimation Using Deep Learning and Geometry" (https://arxiv.org/abs/1612.00496).

self-driving-car cnn-keras convolutional-neural-networks deep-learning bounding-boxes regression kitti-datasetThis CAPTCHAs generated from repo CAPTCHA_generator. Using about 5000 samples, Obtain 95% single letter accuracy.

captcha cnn-keras machine-learningThis project acts as both a tutorial and a demo to using Hyperopt with Keras, TensorFlow and TensorBoard. Not only we try to find the best hyperparameters for the given hyperspace, but also we represent the neural network architecture as hyperparameters that can be tuned. This automates the process of searching for the best neural architecture configuration and hyperparameters. Here, we are meta-optimizing a neural net and its architecture on the CIFAR-100 dataset (100 fine labels), a computer vision task. This code could be easily transferred to another vision dataset or even to another machine learning task.

hyperopt hyperparameter-optimization hyperparameter-tuning hyperparameters-optimization hyperparameter-search keras cnn cnn-keras tensorflowNotebooks covering Intro to CNN, Transfer Learning using VGG16

cnn-lecture keras-practice keras keras-neural-networks cnn-kerasIn the Jupyter notebook for this repository, I begin by calculating the bottleneck features for the CIFAR-10 dataset. These features are then visualized with a Barnes-Hut implementation of t-SNE, which is the fastest t-SNE implementation to date.

keras keras-neural-networks keras-models keras-classification-models keras-visualization images image-classification classification classifier classification-algorithm cnn cnn-keras cnn-model cnn-architecture convolutional-neural-networks convolutional-networks tsne tsne-algorithm visualization transfer-learningThe original Matlab implementation and paper (for AlexNet, GoogLeNet, and VGG16) can be found here. A Keras implementation of VGG-CAM can be found here. This implementation is written in Keras and uses ResNet-50, which was not explored in the original paper.

keras cnn resnet-50 resnet localization cnn-keras cnn-model cnns localisation image-analysis classification image-classification keras-neural-networks keras-tensorflow keras-visualization keras-models keras-classification-models
We have large collection of open source products. Follow the tags from
Tag Cloud >>

Open source products are scattered around the web. Please provide information
about the open source projects you own / you use.
**Add Projects.**