There are 1 repository under nueral-networks topic.
『ゼロから作る Deep Learning ❸』(O'Reilly Japan, 2020)
iOS Core ML implementation of waifu2x
This repository is the collection of research papers in Deep learning, computer vision and NLP.
All my experiments with AI and ML
OpenLabeler is an open source desktop application for annotating objects for AI appplications
ONNX Runtime Server: The ONNX Runtime Server is a server that provides TCP and HTTP/HTTPS REST APIs for ONNX inference.
Training with FP16 weights in PyTorch
This is a project on "Stock-Market-Analysis-And-Forecasting-Using-Deep-Learning" using Pytorch, python, deep learning, gru, plotly
This is a Yoga Pose Estimation App which can be able to detect the yoga pose in real time by using posenet and KNN Classifier. Here the dataset used is custom data set which consists of 3 videos for representing 3 different postures. It is deployed in heroku. One Thing to be noted i.e this will work correctly for all mobile and edge devices.
simple tutorial on Machine Learning with Scikitlearn
SynapseAI Core is a reference implementation of the SynapseAI API running on Habana Gaudi
Proof of the concept implementation of smiles2vec paper
Different deep learning architectures are implemented for time series classification and prediction purposes.
This system provides the facility to convert roughly hand-drawn humam face sketch image on canvas into a realistic face image by using image generative AI in a real-time.
Multilayer Perceptron Neural network for binary classification between two type of breast cancer ("benign" and "malignant" )using Wisconsin Breast Cancer Database
A PureScript, browser-based implementation of simple linear regression.
My Projects Submission to Udacity's Deep Learning Nanodegree Program
Fortran Based Nueral Networks
The program uses HOG and LBP features to detect human in images. First, use the HOG feature only to detect humans. Next, combine the HOG feature with the LBP feature to form an augmented feature (HOG-LBP) to detect human. A Two-Layer Perceptron (feedforward neural network) will be used to classify the input feature vector into human or no-human.
We segmented the Brain tumor using Brats dataset and as we know it is in 3D format we used the slicing method in which we slice the images in 2D form according to its 3 axis and then giving the model for training then combining waits to segment brain tumor. We used UNET model for our segmentation.
Where all the state-of-the-art computer vision Algorithms meet
Deep Learning using Neural Network Toolbox + Finance Portfolio Selection with MorningStar
Predicting the ability of a borrower to pay back the loan through Traditional Machine Learning Models and comparing to Ensembling Methods
Combine CNN and RNN knowledge to build a network that automatically produces captions when given an input image. Python, PyTorch.
Loss Function in PyTorch
NanoNets Object Detection API Example for Node.js
Implementation of TD Gammon algorithm by Gerald Tesauro at IBM's Thomas J. Watson Research Center in Python.
Keras implementation of Deep Convolutional Generative Adversarial Networks, code run base on tensorflow or theano
The code here can be used to train a Transformer Neural Network to perform symbol recovery at the receiver end.
Predict the Next Pandemic Initiative
Graph Neural Network and Machine Learning Analysis of Functional Neuroimaging for Understanding Schizophrenia