Deep neural networks topic
Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural networks are a type of deep learning, which is a type of machine learning. Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and natural language processing. Deep neural networks are used in a variety of applications, including speech recognition, computer vision, and natural language processing.
unofficial-implement-of-openpose
Implement of Openpose use Tensorflow
nn-Meter
A DNN inference latency prediction toolkit for accurately modeling and predicting the latency on diverse edge devices.
noreward-rl
[ICML 2017] TensorFlow code for Curiosity-driven Exploration for Deep Reinforcement Learning
linorobot
Autonomous ground robots (2WD, 4WD, Ackermann Steering, Mecanum Drive)
rad
RAD: Reinforcement Learning with Augmented Data
SqueezeSeg
Implementation of SqueezeSeg, convolutional neural networks for LiDAR point clout segmentation
pix2code
pix2code: Generating Code from a Graphical User Interface Screenshot
self-driving-car
Udacity Self-Driving Car Engineer Nanodegree projects.
kur
Descriptive Deep Learning
saliency
Framework-agnostic implementation for state-of-the-art saliency methods (XRAI, BlurIG, SmoothGrad, and more).
pyconv
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition (https://arxiv.org/pdf/2006.11538.pdf)