{"id":651,"date":"2020-08-24T09:01:04","date_gmt":"2020-08-24T09:01:04","guid":{"rendered":"https:\/\/data-science.gotoauthority.com\/2020\/08\/24\/how-to-handle-overfitting-in-deep-learning-models\/"},"modified":"2020-08-24T09:01:04","modified_gmt":"2020-08-24T09:01:04","slug":"how-to-handle-overfitting-in-deep-learning-models","status":"publish","type":"post","link":"https:\/\/wealthrevelation.com\/data-science\/2020\/08\/24\/how-to-handle-overfitting-in-deep-learning-models\/","title":{"rendered":"How to Handle Overfitting In Deep Learning Models"},"content":{"rendered":"
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Deep learning is one of the most revolutionary technologies at present. It gives machines the ability to think and learn on their own. The key motivation for deep learning is to build algorithms that mimic the human brain<\/strong>.\u00a0<\/p>\n

To achieve this we need to feed<\/strong> as much as relevant data<\/strong> for the models to learn. Unlike machine learning algorithms<\/a> the deep learning algorithms learning won\u2019t be saturated<\/strong> with feeding more data. But feeding more data to deep learning models will lead to overfitting<\/strong> issue.<\/p>\n

That\u2019s why developing a more generalized deep learning model is always a challenging problem to solve. Usually, we need more data<\/strong> to train the deep learning model. In order to get an efficient score we have to feed more data to the model. But unfortunately, in some cases, we face issues with a lack of data<\/a>.\u00a0<\/p>\n

One of the most common problems with building neural networks is overfitting. The key reason is, the build model is not generalized well and it\u2019s well-optimized only for the training dataset. In layman terms, the model memorized how to predict the target class only for the training dataset.\u00a0<\/p>\n

The other cases overfitting usually happens when we don\u2019t have enough<\/strong> data, or because of complex architectures without regularizations<\/strong>.<\/p>\n

If we don’t have the sufficient data to feed, the model will fail to capture the trend in data. It tries to understand each and every data point in training data and performs poorly on test\/unseen data.\u00a0<\/p>\n<\/div>\n

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Learn how to handle overfitting in deep learning models.<\/p>\n<\/div>\n

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In some cases, the model is overfitted if we use very complex neural network architecture without applying proper data preprocessing<\/strong> techniques to handling the overfitting.<\/p>\n

So we need to learn how to apply smart techniques to preprocess the data before we start building<\/strong> the deep learning models. These techniques we are going to see in the next section in the article.<\/p>\n

In this article, you are going to learn how smartly we can handle overfitting in deep learning<\/a>, this helps to build the best and highly accurate models.<\/p>\n

Before we drive further let\u2019s see what you learning in this article.<\/p>\n<\/div>\n

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Deep learning Introduction<\/h2>\n

High-end research is happening in the deep learning field, every day some new features or new model architecture or well-optimized models were going up to give continuous updates in this field. This makes the deep learning field young all the time, its growth rate is exponentially increasing.<\/p>\n

The growth of this field is reasonable and expected one too. If we observe, In the past two decades back, we had problems like storing data, data scarcity, lack of \u00a0high computing processors, cost of processors, etc.\u00a0<\/p>\n

At present, the scenario was completely different. Big data<\/strong> came into picture which allows you to store huge amounts of data so easily. We are having very powerful computing processors with very low\/cheap cost. And also we can solve almost any problem with the help of neural networks<\/a>.\u00a0<\/p>\n

Deep learning algorithms have a lot of different architectures<\/strong> like\u00a0<\/p>\n