", df_ge. isna(). sum())Normalizing the dataThe data is not normalized and the range for each column varies, especially Volume. Normalizing data…

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## Failing Fast with DeepAR Neural Networks for Time-Series

Failing Fast with DeepAR Neural Networks for Time-SeriesMatt WheelerBlockedUnblockFollowFollowingFeb 26Harness machine learning algorithms on AWS to build out reliable time…

Continue Reading## Lambda architecture— how to build a Big data pipeline part 1

With a large number of smart devices generating a huge amount of data, it would be ideal to have a…

Continue Reading## AWS S3 Batch Operations: Beginner’s Guide

Let’s get going. Accessing the PreviewIf you don’t have access to S3 batch operations preview, fill in the form in this…

Continue Reading## How to Control the Speed and Stability of Training Neural Networks With Gradient Descent Batch Size

Neural networks are trained using gradient descent where the estimate of the error used to update the weights is calculated…

Continue Reading## How to Accelerate Learning of Deep Neural Networks With Batch Normalization

Batch normalization is a technique designed to automatically standardize the inputs to a layer in a deep learning neural network.…

Continue Reading## Accelerate the Training of Deep Neural Networks with Batch Normalization

Training deep neural networks with tens of layers is challenging as they can be sensitive to the initial random weights…

Continue Reading## Understanding the 3 Primary Types of Gradient Descent

Mini Batch Gradient Descent is commonly used for deep learning problems.ConclusionThis article should give you the basic motivation for the…

Continue Reading## How Much Does Training Scales?

In practice, data scientists tend to play with different batch sizes and see what works but those methods often result…

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