A resource-conscious neural network implementation for MCUs
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Updated
Dec 22, 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.
A Handwritten Digit Recognizer on the Web. Model trained locally on MNIST with ANN built from scratch.
All my machine learning projects and tests.
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
Neural Network Applications
Digit Recognition on MNIST Data
A simple handwritten digit classifier NN implemented from scratch in C++.
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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