mnist
Here are 36 public repositories matching this topic...
Learning with Signatures
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Jun 1, 2022 - HTML
Experiments on cluttered mnist dataset with Tensorflow.
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Feb 3, 2017 - HTML
Canvas to JSON, using Tensorflow & MNIST to predict digit
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Oct 6, 2018 - HTML
Derin Öğrenme kullanarak el yazısıyla yazılmış rakamları tanımak için yazılmış bir Flask uygulamasıdır.
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Dec 5, 2020 - HTML
Django + DRF + JavaScript (Angular or React) + Machine Learning + Anaconda
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Feb 3, 2024 - HTML
python | deep learning | neural networks
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Dec 20, 2019 - HTML
Generazione di Immagini Avversariali in Tensorflow
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Nov 12, 2018 - HTML
Use GANs with normalization techniques like dropouts, batch normalization along with having a low variance in kernel weight initialization, achieve realistic images of faces trained on the CelebA dataset. Images also have been generated of hand written digits after being trained on the MNIST dataset. This would be useful for generating training …
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Jul 14, 2018 - HTML
Rust numeral recognition using MNIST data
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Jan 9, 2022 - HTML
Code for Model Pruning on MNIST Dataset
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Aug 11, 2019 - HTML
Made with the ONNX.js framework from Microsoft. Similar to tensorflow.js, ONNX.js is another framework to provide the capability of running machine learning models on the web with JavaScript
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Oct 21, 2020 - HTML
Neural network classifier to classify fashion MNIST from scratch. All the calculation are done using NumPy library.
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Jun 17, 2022 - HTML
Our project builds a Convolutional Neural Network (CNN) model to accurately classify handwritten digits from the MNIST dataset. We preprocess the data and design a CNN architecture. Additionally, we create a user-friendly web interface using Flask for easy digit classification.
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Mar 7, 2024 - HTML
Holds code written for MATH 384: Algorithms for Decision-Making at St. Olaf College
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Dec 20, 2016 - HTML
🔮😎 Keras mnist model served through a serverless endpoint
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Nov 18, 2017 - HTML
Project 2: Udacity Deep Learning Image classification
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Mar 7, 2017 - HTML
Leveraging the mapreduce paradigm we propose a solution to parallelize the feedforward operation of neural networks in order to speed it up for sufficiently large NN architectures and for sufficiently large datasets. Tested Using the MNIST dataset results can be found in the results.html and results.ipynb files.
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Jan 7, 2023 - HTML
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