hyperdeveloperr's Stars
beldmitr/i4004
Compiler, IDE and simulator for Intel4004
chrreisinger/OpenVC
OpenVC, an open source VHDL compiler/simulator
RyanPridgeon/solarsystem
3D OpenGL C++ Solar System Simulator
umutphp/backend-best-practices
Backend uygulamaları geliştirirken dikkate alınabilecek örnek yöntemlerin derlendiği güncellenen bir kaynak.
corese4rch/cvurl
cVurl is an open-source wrapper for the Java HTTP client. It is written in Java 11 and can be used with any JDK 11.0.2 or newer.
msracver/Flow-Guided-Feature-Aggregation
Flow-Guided Feature Aggregation for Video Object Detection
dataliterate/sketch-bounding-boxer
Toggle visibility of 'boundingBox' layers in a Sketch document
sampepose/flownet2-tf
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
stevenlin1015/HumanDetection_ML
To check whether human exists in image(s) using CNN base on Machine Learning.
pranoy-panda/Action-recognition-tensorflow
Designing a 3D CNN architecture for action recognition in video
sebastianruder/NLP-progress
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
opencv/opencv
Open Source Computer Vision Library
amineHorseman/facial-expression-recognition-using-cnn
Deep facial expressions recognition using Opencv and Tensorflow. Recognizing facial expressions from images or camera stream
Shirhe-Lyh/multi_task_test
Use tensorflow.contrib.slim to training a simple CNN classification model for multi task
JoeyBoyi/chinese-text-multi-classification-clstm
A Multi-classification of chinese text with cnn-rnn model.
Chevalier1024/MPCNN
Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Network复现
freakandstein/face-recognition-motion-detection
Machine Learning for face recogntion and aditional feature for motion detector
smitshilu/AISecurityCamera
A simple Security Camera example which detects motion and perform face recognition
khushboo-agarwal/Action-Recognition
recognize actions from videos using machine learning classifier(s) and suitable features. You will use UCF sports action data set here http://crcv.ucf.edu/data/ucf_sports_actions.zip. UCF Sports dataset consists of a set of actions collected from various sports which are typically featured on broadcast television channels such as the BBC and ESPN. The video sequences were obtained from a wide range of stock footage websites including BBC Motion gallery and GettyImages. The dataset includes a total of 150 sequences with the resolution of 720 x 480. The collection represents a natural pool of actions featured in a wide range of scenes and viewpoints. By releasing the data set we hope to encourage further research into this class of action recognition in unconstrained environments. Since its introduction, the dataset has been used for numerous applications such as: action recognition, action localization, and saliency detection. The dataset includes the following 10 actions. The figure above shows the a sample frame of all ten actions, along with their bounding box annotations of the humans shown in yellow.
shivakrishna2497/Predicting-the-on-site-of-Diabetes-using-Keras-using-
I collected the Pima Indians onset of diabetes dataset from UCI Machine Learning repository,It describes patient medical record data for Pima Indians and whether they had an onset of diabetes within five years, As such, it is a binary classification problem (onset of diabetes as 1 or not as 0). I built my first neural network using keras which takes numerical input and numerical output Number of Instances: 768 Number of Attributes: 8 plus class For Each Attribute: (all numeric-valued) 1. Number of times pregnant 2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test 3. Diastolic blood pressure (mm Hg) 4. Triceps skin fold thickness (mm) 5. 2-Hour serum insulin (mu U/ml) 6. Body mass index (weight in kg/(height in m)^2) 7. Diabetes pedigree function 8. Age (years) 9. Class variable (0 or 1) Steps I followed in building a neural network using Keras: 1)Load Data I have loaded the file directly using the NumPy function loadtxt(). There are eight input variables and one output variable ,Once loaded I split the dataset into input variables (X) and the output class variable (Y) 2)Define Network I created a Sequential() model and added layers one at a time , first layer has 12 neurons and expects 8 input variables, second hidden layer has 8 neurons and finally, the output layer has 1 neuron to predict the class (onset of diabetes or not) 3) Compile Network I Compiled the model using tensorflow as back-end,I used “binary_crossentropy“(loss function) and default gradient descent algorithm “adam” 4)Fit Network I fit the network with my training set(80%) by calling the fit() function on the model,For this problem, I gave a small number of epochs=150 and a small batch size of 10 5)Evaluate Network I evaluated the performance of the network on the same training dataset and got a training accuracy of 76.55% and after making predictions in next step I evaluated the performance of the network using testdataset where I got a testing accuracy of 76.62% 6)Make Predictions predictions will be in the range between 0 and 1 as there's a sigmoid activation function on the output layer and I converted them into a binary prediction for this classification task by rounding them
lsimmons2/bmi-project
machine learning model that predicts body mass index from face images
s-neilson/CORNISH-CNN-classifier
A convolutional neural network for the classification of astronomical obejcts using data that is part of the CORNISH (Co-Ordinated Radio 'N' Infrared Survey for High-mass star formation) project (website: http://cornish.leeds.ac.uk/public/index.php).
xiaochus/FaceRecognition
OpenCV 3 & Keras implementation of face recognition for specific people.
dclambert/Python-ELM
Extreme Learning Machine implementation in Python
uchidalab/dtw-features-cnn
Introducing Local Distance-based Features to Temporal Convolutional Neural Networks (ICFHR 2018)
sagarverma/EgoActionRecognition
Action recognition in first person (egocentric) videos.
gabarlacchi/MASK-CNN-for-actions-recognition-
Actions recognition in sport videos (football, basketball dancing ect)
fennialesmana/extreme-learning-machine
The implementation of Extreme Learning Machine (ELM) classification algorithm by (Huang, et al., 2006)
chickenbestlover/Online-Recurrent-Extreme-Learning-Machine
Online-Recurrent-Extreme-Learning-Machine (OR-ELM) for time-series prediction, implemented in python
PriyeshV/HOPF
HOPF: Higher Order Propagation Framework and Fusion Graph Convolutional Networks