A resource-conscious neural network implementation for MCUs
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Updated
Jul 30, 2024 - C++
A resource-conscious neural network implementation for MCUs
Tensorflow Tutorial files and Implementations of various Deep NLP and CV Models.
Implementation of an AlphaGo Zero paper in one C++ header file without any dependencies
Examples of PHP-FANN OCR Neural Networks
Problems Identification: This project involves the implementation of efficient and effective KNN classifiers on MNIST data set. The MNIST data comprises of digital images of several digits ranging from 0 to 9. Each image is 28 x 28 pixels. Thus, the data set has 10 levels of classes.
All my machine learning projects and tests.
A Handwritten Digit Recognizer on the Web. Model trained locally on MNIST with ANN built from scratch.
THE MNIST DATABASE of handwritten digits Yann LeCun, Courant Institute, NYU
Xgboost-PyTorch Models on MNIST with K8s SageMaker Operators
Simple NN for MNIST Recognition
Digit Recognition on MNIST Data
A simple handwritten digit classifier NN implemented from scratch in C++.
Neural Network Applications
Image Recognition using Python on MNIST dataset with the help of CNN, Multiclass Logistic Regression and SGD
A deep learning implementation to recognize single integers from integers. Implemented with tensorflow. **Requires IPython Notebooks to be run ;)
MNIST Digits Classification with numpy only
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