Building Accurate Neural Networks for Machine Learning

This videos explains the problems of overfitting and underfitting as related to neural networks, and provides you with some techniques to combat these effects so you can build an accurate artificially intelligent model.

Follow the rest of this Machine Learning & Neural Network playlist to learn how to develop your own artificially intelligent systems:
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Chapters in this video:
0:00 Intro
0:31 Overview
1:06 Overfitting & Underfitting
4:16 Data Augmentation
6:13 Dropout
7:50 Weight Regularization & Decay
11:50 Batch Normalization
13:54 Hypertuning
17:25 Custom (Lambda) Layer
19:30 Outro