# Solving classification task with deep network

I am currently working on a classification problem:

The dataset (2000 features, 25 labels) I am using is seperable by using a 2-layered Multi-Layer-Perceptron (1 Input + 1 OutputLayer = 1 Weight Matrix) with achieving great accuracy.

Note: The Data I am using are spectrograms.

Now what I am curious about is the following: Can I use a deep network (more than 2 layers) to even increase the accuracy?

What I think about: Increasing the network structure would make the network interpret the data in a more complex way and therefore should be able to even correctly classify little "outbrakers" and "noisy elements" of the dataset.

• Whenever you add more layers, there will be vanishing and exploding gradients which may cause your network not to learn, or learning may happen so slowly. To avoid, you should use ReLU activation in order to avoid saturation of gradients. Moreover you have to use He or Xavier initialization techniques to avoid having bad random weights. There are other techniques for solving this problem which are called skip connections but at least I've never seen the use of them in MLPs Although they are really helpful for solving the mentioned problem.
• Overfitting is a problem that happens for large architectures that are not fed with enough training data. You should use regularization techniques. There are so many papers about this but in your case which is using MLP I highly recommend you using Dropout technique which is invented by the so called God Father of deep learning.