{"id":241975,"date":"2020-04-30T07:55:15","date_gmt":"2020-04-30T07:55:15","guid":{"rendered":"https:\/\/imarticus.org\/?p=241975"},"modified":"2021-09-20T05:06:21","modified_gmt":"2021-09-20T05:06:21","slug":"what-are-some-tips-and-tricks-for-training-deep-neural-networks","status":"publish","type":"post","link":"https:\/\/imarticus.org\/blog\/what-are-some-tips-and-tricks-for-training-deep-neural-networks\/","title":{"rendered":"What Are Some Tips And Tricks For Training Deep Neural Networks?"},"content":{"rendered":"

Deep Neural Networks aid AI applications such as image and voice recognition to function at unprecedented accuracy. A Deep Neural network is basically an array of several layers, where each layer sieves raw data into a structured mathematical model.\u00a0<\/span><\/p>\n

The process of making the data flow through the various layers is called Deep Neural Network Training. In humans, we also start recognizing an object once we have seen it several times. If you saw just one \u201ccar\u201d in your entire life, you might not be able to recognize a car again if you saw a different model this time.\u00a0<\/span><\/p>\n

In Data Science, this is easier said than done. Therefore, we have some tips and tricks that you can use when you sit down to teach your DNN to distinguish cars from trucks.<\/span><\/p>\n

Normalization is Effective<\/b><\/p>\n

Normalization layers help group logical data points into a higher consolidated structure. An apparent increase in performance has been recorded when using Normalization.<\/span><\/p>\n

You can use it three ways;<\/strong><\/p>\n