Graphical Neural Networks in Python: Building a Simple MNIST Classifier
Graphical Neural Networks in Python: Building a Simple MNIST Classifier
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Learn how to build and train a graphical neural network from scratch using Python. We'll be implementing a Convolutional Neural Network (CNN) to solve the MNIST handwritten digit recognition problem. This tutorial assumes a basic understanding of linear algebra and Python programming. For a more comprehensive learning experience, check out the official TensorFlow and scikit-learn documentation.
In this post, we'll discuss the theory behind graphical neural networks, the architecture of a CNN, and how to train and evaluate a model using Python and NumPy. We'll begin by importing necessary libraries and implementing our network's placeholders and weights. Next, we'll write functions for our convolution and activation functions and create a forward pass function. We'll conclude by compiling our model, iterating through the training dataset, and assessing model performance.
Additional Resources:
1. TensorFlow: https://www.tensorflow.org/
2. Scikit-learn: https://scikit-learn.org/stable/
3. Offical MNIST Dataset: http://yann.lecun.com/exdb/mnist/
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