271 skills found · Page 2 of 10
zhouchunpong / GCN Keras图卷积神经网络 Graph Convolutional Network with Keras
kstaats / Karoo GpA Genetic Programming platform for Python with TensorFlow for wicked-fast CPU and GPU support.
pythonlessons / TensorFlow Object Detection TutorialThe purpose of this tutorial is to learn how to install and prepare TensorFlow framework to train your own convolutional neural network object detection classifier for multiple objects, starting from scratch
kartikmadan11 / MetaTraderForecastRNN based Forecasting App for Meta Trader and similar trading platforms
datamachines / Cuda Tensorflow OpencvDockerFile with GPU support for TensorFlow and OpenCV
inoryy / Tensorflow Optimized WheelsTensorFlow wheels built for latest CUDA/CuDNN and enabled performance flags: SSE, AVX, FMA; XLA
mm909 / Kaggle AutismDetecting Autism Spectrum Disorder in Children With Computer Vision - Adapting facial recognition models to detect Autism Spectrum Disorder
MycChiu / Fast LayerNorm TFEfficient layer normalization GPU kernel for Tensorflow
nathtest / Tutorial Ubuntu 18.04 Install Nvidia Driver And CUDA And CUDNN And Build Tensorflow For GpuUbuntu 18.04 How to install Nvidia driver + CUDA + CUDNN + build tensorflow for gpu step by step command line
zylo117 / Tensorflow Gpu MacosxUnoffcial NVIDIA CUDA GPU support version of Google Tensorflow for MAC OSX
frotms / MobilenetV3 Tensorflowthe multi-GPUs implementation of mobilenet v3 in tensorflow with tf.layers
WindQAQ / Tf Recsystf-recsys contains collaborative filtering (CF) model based on famous SVD and SVD++ algorithm. Both of them are implemented by tensorflow in order to utilize GPU acceleration.
21Vipin / Medical Image Classification Using Deep LearningTumour is formed in human body by abnormal cell multiplication in the tissue. Early detection of tumors and classifying them to Benign and malignant tumours is important in order to prevent its further growth. MRI (Magnetic Resonance Imaging) is a medical imaging technique used by radiologists to study and analyse medical images. Doing critical analysis manually can create unnecessary delay and also the accuracy for the same will be very less due to human errors. The main objective of this project is to apply machine learning techniques to make systems capable enough to perform such critical analysis faster with higher accuracy and efficiency levels. This research work is been done on te existing architecture of convolution neural network which can identify the tumour from MRI image. The Convolution Neural Network was implemented using Keras and TensorFlow, accelerated by NVIDIA Tesla K40 GPU. Using REMBRANDT as the dataset for implementation, the Classification accuracy accuired for AlexNet and ZFNet are 63.56% and 84.42% respectively.
guillaume-chevalier / GloVe As A TensorFlow Embedding LayerTaking a pretrained GloVe model, and using it as a TensorFlow embedding weight layer **inside the GPU**. Therefore, you only need to send the index of the words through the GPU data transfer bus, reducing data transfer overhead.
daddydrac / Deep Learning UltraOpen source Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in PyTorch, OpenCV (compiled for GPU), TensorFlow 2 for GPU, PyG and NVIDIA RAPIDS
MiguelMonteiro / Permutohedral LatticePermutohedral Lattice C++/CUDA implementation + TensorFlow Op (CPU/GPU)
golbin / TensorFlow Multi GPUsSamples for Multi GPUs in TensorFlow
armaanpriyadarshan / Training A Custom TensorFlow 2.X Object DetectorLearn how to train a TensorFlow Custom Object Detector with TensorFlow-GPU
PINTO0309 / TensorflowLite UNetImplementation of UNet by Tensorflow Lite. Semantic segmentation without using GPU with RaspberryPi + Python. In order to maximize the learning efficiency of the model, this learns only the "Person" class of VOC2012. And Comparison with ENet.
gouthamvgk / Facemesh Coreml TfThis repository contains the code for converting tflite model of Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs and Blazeface to tensorflow and coreml.
