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Project Details

Fine-Tuning Shallow Neural Networks with Keras

Achieve state-of-the-art image classification without the long training times

The Gradient Team


Neural networks with extensively deep architectures typically contain millions of parameters, making them both computationally expensive and time-consuming to train.

In this tutorial, we'll achieve state-of-the-art image classification performance using DenseNet, initially with a single hidden layer. We systematically tune the number of neurons in the hidden layer and train our model on a benchmark image classification dataset. This study shows that building deeper neural networks is not always necessary; instead, it is more important to focus on the correct number of neurons in each layer.

For a more detailed breakdown of the code, check out the full tutorial on the blog.