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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

By
The Gradient Team

Description

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.