In this codelab, you will learn how to build and train a neural network that recognises handwritten digits. Along the way, as you enhance your neural network to achieve 99% accuracy, you will also discover the tools of the trade that deep learning professionals use to train their models efficiently.
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In this codelab, you will learn how to build and train a neural network that recognises handwritten digits. Along the way, as you enhance your neural network to achieve 99% accuracy, you will also discover the tools of the trade that deep learning professionals use to train their models efficiently.
This codelab uses the MNIST dataset, a collection of 60,000 labeled digits that has kept generations of PhDs busy for almost two decades. You will solve the problem with less than 100 lines of Python / TensorFlow code.
What you'll learn
- What is a neural network and how to train it
- How to build a basic 1-layer neural network using tf.keras
- How to add more layers
- How to set up a learning rate schedule
- How to build convolutional neural networks
- How to use regularisation techniques: dropout, batch normalization
- What is overfitting
What you'll need
Just a browser. This workshop can be run entirely with Google Colaboratory.
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