GitHub / anh-nn01 / Neural-Network-from-Scratch--Hand-written-Digits-classifier
This is my first Deep Learning project, which is a MNIST hand-written digits classifier. The model is implemented completely from scratch WITHOUT using any prebuilt optimization like Tensorflow or Pytorch. Tensorflow is imported only to load the MNIST data set. This model also uses 2 hidden layers with Adaptive Moment Optimization (Adam) and Drop-out regularization.
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PURL: pkg:github/anh-nn01/Neural-Network-from-Scratch--Hand-written-Digits-classifier
Stars: 8
Forks: 0
Open issues: 0
License: None
Language: Jupyter Notebook
Size: 5.9 MB
Dependencies parsed at: Pending
Created at: over 5 years ago
Updated at: almost 2 years ago
Pushed at: about 5 years ago
Last synced at: almost 2 years ago
Topics: artificial-intelligence, deep-learning, machine-learning, mnist, mnist-classification, neural-network, neural-networks-from-scratch, scratch-implementation