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16 search results found

Coursera Deep Learning Specialization ⭐ 2,459

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

Dropblock ⭐ 408

Implementation of DropBlock: A regularization method for convolutional networks in PyTorch.

Deepnet ⭐ 267

Implementations of CNNs, RNNs and deep learning techniques in pure Numpy

3d Mri Brain Tumor Segmentation Using Autoencoder Regularization ⭐ 243

Keras implementation of the paper "3D MRI brain tumor segmentation using autoencoder regularization" by Myronenko A. (https://arxiv.org/abs/1810.11654).

Liteflownet2 ⭐ 230

A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularization, TPAMI 2020

Deep Learning Specialization Coursera ⭐ 92

Deep Learning Specialization courses by Andrew Ng, deeplearning.ai

a Ready-to-use PyTorch Extension of Unofficial CutMix Implementations with more improved performance.

Numpy Neuralnet Exercise ⭐ 42

Implementation of key concepts of neuralnetwork via numpy

Repo for our CVPR Paper: Watch your Up-Convolution: CNN Based Generative Deep Neural Networks areFailing to Reproduce Spectral Distributions

Deep Learning Specialization Coursera ⭐ 36

Deep Learning Specialization Course by Coursera. Neural Networks, Deep Learning, Hyper Tuning, Regularization, Optimization, Data Processing, Convolutional NN, Sequence Models are including this Course.

Cnn Project ⭐ 30

Project for CS 231N

3d Brain Tumor Segmentation ⭐ 19

Volumetric MRI brain tumor segmentation using autoencoder regularization

Multi Label Classification ⭐ 18

基于tf.keras的多标签多分类模型

SANNet Neural Network Framework

Simple Implementation Of Ml Algorithms ⭐ 17

My simplest implementations of common ML algorithms

Deeplearning_from_scratch ⭐ 16

A Deep Learning framework for CNNs and LSTMs from scratch, using NumPy.

Deep Learning Notes ⭐ 15

Deep Learning Coursera Specialization • Lecture Notes • Lab Assignments

Visualdialog Pytorch ⭐ 13

Community Regularization of Visually Grounded Dialog https://arxiv.org/abs/1808.04359

Deeplearninghandbook ⭐ 12

Lecture Slides and Programming Exercises that may help study the deep learning book by Goodfellow, Bengio and Courville.

Traffic Sign Cnn ⭐ 12

Deep learning network for traffic sign image classification

Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks

Deepexperiments ⭐ 9

TensorFlow/Keras experiments on computer vision and natural language processing

Neural Networks And Deep Learning ⭐ 9

Deep learning projects including applications (face recognition, neural style transfer, autonomous driving, sign language reading, music generation, translation, speech recognition and NLP) and theories (CNNs, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, hyperparameter tuning, regularization, optimization, Residual Networks). Deep Learning Specialization by Andrew Ng, deeplearning.ai

Robust Representation ⭐ 8

Fully_unsupervised_cnn_registration_keras ⭐ 8

Fully unsupervised 2D/3D image registration with ConvNet.

Coursera Deep Learning Specialization ⭐ 7

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks..

Shake Shake Tensorflow ⭐ 7

Simple Code Implementation of "Shake-Shake Regularization using TensorFlow.

Deep Learning ⭐ 7

Implemented the deep learning techniques using Google Tensorflow that cover deep neural networks with a fully connected network using SGD and ReLUs; Regularization with a multi-layer neural network using ReLUs, L2-regularization, and dropout, to prevent overfitting; Convolutional Neural Networks (CNNs) with learning rate decay and dropout; and Recurrent Neural Networks (RNNs) for text and sequences with Long Short-Term Memory (LSTM) networks.

Dropblock_mxnet_bottom_implemention ⭐ 6

用C++实现一个mxnet版本dropblock Op 最后可以用mx.sym.Dropblock（）调用

Library which can be used to build feed forward NN, Convolutional Nets, Linear Regression, and Logistic Regression Models.

A C++ deep learning library for the construction and optimization of neural networks ranging from simple feedforward architectures to state-of-the-art convolutional ResNets and LSTMs.

Sk Regularization ⭐ 6

Code for "Learning a smooth kernel regularizer for convolutional neural networks" (Feinman & Lake, 2019)

Keras Dropblock ⭐ 5

DropBlock:A regularization method for convolutional networks

Gcn.pytorch ⭐ 5

Graph Convolutional Networks for Text Classification.

Tensornet ⭐ 5

A high-level deep learning library built on top of PyTorch.

Dropblock Keras Implementation ⭐ 5

Paper Reproduction: "DropBlock: A regularization method for convolutional networks"

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