687 Open source projects that are alternatives of or similar to Keras English Resume Parser And Analyzer

A recurrent attention module consisting of an LSTM cell which can query its own past cell states by the means of windowed multi-head attention. The formulas are derived from the BN-LSTM and the Transformer Network. The LARNN cell with attention can be easily used inside a loop on the cell state, just like any other RNN. (LARNN)

Nonrigid image registration using multi-scale 3D convolutional neural networks

A journey into Convolutional Neural Network visualization

Naszilla is a Python library for neural architecture search (NAS)

Image reconstruction done with untrained neural networks.

TensorFlow implementation of AlexNet and its training and testing on ImageNet ILSVRC 2012 dataset

A curated list of Deep Learning resources sorted by reputation

All Convolutional Network: (https://arxiv.org/abs/1412.6806#) implementation in Keras

This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Network algorithm, to predict stock prices.

[ICML 2018] "Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions"

OSMDeepOD - OpenStreetMap (OSM) and Machine Learning (Deep Learning) based Object Detection from Aerial Imagery (Formerly also known as "OSM-Crosswalk-Detection").

Code for the paper Benchmark Analysis of Representative Deep Neural Network Architectures

Tensorflow Implementation of Recurrent Neural Network (Vanilla, LSTM, GRU) for Text Classification

Improved Residual Networks (https://arxiv.org/pdf/2004.04989.pdf)

Computer Vision library for human-computer interaction. It implements Head Pose and Gaze Direction Estimation Using Convolutional Neural Networks, Skin Detection through Backprojection, Motion Detection and Tracking, Saliency Map.

The first public PyTorch implementation of Attentive Recurrent Comparators

a collection of my notes on deep learning

A deep learning framework for molecular docking

ResNet with Shift, Depthwise, or Convolutional Operations for CIFAR-100, CIFAR-10 on PyTorch

Fast Neural Style Transfer implementation in PyTorch 🎨 🎨 🎨

Assignment solutions for the CS231n course taught by Stanford on visual recognition. Spring 2017 solutions are for both deep learning frameworks: TensorFlow and PyTorch.

SRU is a recurrent unit that can run over 10 times faster than cuDNN LSTM, without loss of accuracy tested on many tasks.

DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processing (ICCV 2019)

Real time face alignment

实现常用基于深度学习的人脸检测算法

Spying using Smartwatch and Deep Learning

Using Machine Learning to predict the type of exercise from movement data

This repository contains the code of LiviaNET, a 3D fully convolutional neural network that was employed in our work: "3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study"

NLTK Source

My assignment solutions for CS231n - Convolutional Neural Networks for Visual Recognition

Multi-Perspective Convolutional Neural Networks for modeling textual similarity (He et al., EMNLP 2015)

[ACM MM 2018] Attribute-Aware Attention Model for Fine-grained Representation Learning

Automated Driving in NFS using CNN.

Caffe implementation of "Fast and Accurate Single Image Super-Resolution via Information Distillation Network" (CVPR 2018)

A machine learning experiment

Detect and recognize The Simpsons characters using Keras and Faster R-CNN

Keras implementation of video classifier

Efficient Sparse-Winograd Convolutional Neural Networks (ICLR 2018)

Implementation of Convolutional Neural Networks for Sentence Classification (Y.Kim, EMNLP 2014) on Pytorch.

pip install antialiased-cnns to improve stability and accuracy

A specially designed light version of Fast AutoAugment

The code for the MaD TwinNet. Demo page:

Learning the Enigma with Recurrent Neural Networks

Perform deep neural network based face detection and recognition in the cloud (via AWS lambda) with zero model configuration or tuning.

Keras implementation of Legendre Memory Units

An Echo State Network module for PyTorch.

Human Activity Recognition (HAR) with 1D Convolutional Neural Network in Python and Keras

papers on scalable and efficient machine learning systems

Weakly Supervised Deep Detection Networks (CVPR 2016)

CVPR2014-Convolutional neural networks for no-reference image quality assessment

MotionSense Dataset for Human Activity and Attribute Recognition ( time-series data generated by smartphone's sensors: accelerometer and gyroscope)

pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit.

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

Generate slow-motion videos by interpolating more frames

Lyrics Generator aka Character-level Language Modeling with Multi-layer LSTM Recurrent Neural Network

How to build RNNs and LSTMs from scratch with NumPy.

Handwritten Digit Recognition using Machine Learning and Deep Learning

This is an implementation of Bilinear CNN for fine grained visual recognition using TensorFlow.

Effective Video Augmentation Techniques for Training Convolutional Neural Networks