Popular GitHub Repositories for Leaky Relu

校招、秋招、春招、实习好项目！带你从零实现一个高性能的深度学习推理库，支持大模型 llama2 、Unet、Yolov5、Resnet等模型的推理。Implement a high-performance deep learning inference library step by step

Building Convolutional Neural Networks From Scratch using NumPy

Represent trained machine learning models as Pyomo optimization formulations

Rede Neural Convolucional para reconhecimento de gestos em LIBRAS (Alfabeto) Projeto 01/2019 - Ciência da Computa??o (Universidade Anhembi Morumbi)

Building a HTTP-accessed convolutional neural network model using TensorFlow NN (tf.nn), CIFAR10 dataset, Python and Flask.

Implementation of key concepts of neuralnetwork via numpy

Vision Transformers Needs Registers. And Gated MLPs. And +20M params. Tiny modality gap ensues!

Covid-19 detection in chest x-ray images using Convolution Neural Network.

Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence

The project aimed to implement Deep NN / RNN based solution in order to develop flexible methods that are able to adaptively fillin, backfill, and predict time-series using a large number of heterogeneous training datasets.

Deep Learning using Rectified Linear Units (ReLU)

QReLU and m-QReLU: Two novel quantum activation functions for Deep Learning in TensorFlow, Keras, and PyTorch

Simple MATLAB toolbox for deep learning network: Version 1.0.3

Convolutional autoencoder for encoding/decoding RGB images in TensorFlow with high compression ratio

Recognizing the Digits from 0-9 using their pixel values as attributes, using Deep Learning Model to Classify the Digits.

An algorithm that facilitates communication between a speech-impaired person and someone who doesn't understand sign language using convolution neural networks

Super Resolution's the images by 3x using CNN

Avoiding the vanishing gradients problem by adding random noise and batch normalization

A classifier to differentiate between Cat and Non-Cat Images

Rede convolucional em C. Camadas Conv, ConvNc, Pool, PoolAv, Relu, Softmax, BatchNorm, DropOut, FullConnect.