Applying MLP to the MNIST dataset

Let's apply MLP to the MNIST dataset.

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Let us discuss our first deep neural network for handwritten character recognition, using the $\text{MNIST}$ dataset. This dataset has dominated much of the early $\text{DNN}$ developments and must be considered a classic by now. The data set consists of digitized examples of handwritten numbers from 0 to 9. Each image consists of $28 \times 28$ pixels. An example is shown as input in the figure below. This benchmark dataset has $60,000$ training examples and $10,000$ test examples. A developer is free to choose any portion of the $60,000$ training images as the validation set.

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Introduction

Scientific Programming with Python

Machine Learning with Sklearn

Neural Networks and Keras

Regression and Optimization

Basic Probability Theory

Probabilistic Regression and Bayes Nets

Generative Models

Cyclic Models and Recurrent Neural Networks

Reinforcement Learning

Artificial intelligence, the Brain, and Our Society

Conclusion

Applying MLP to the MNIST dataset

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