A.I, Data and Software Engineering

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Recurrent neural network – predict monthly milk production

Recurrent neural network

In part 1, we introduced a simple RNN for time-series data. To continue, this article applies a deep version of RNN on a real dataset to predict monthly milk production. The data Monthly milk production: pounds per cow. Jan 1962 – Dec 1975. You can download the data using this link. Download: CSV file The data contains the production of 168 months (14 years). We will use an RNN to predict...

Recurrent neural network – time-series data- part 1

RNN block

If you are human and curious about your future, then the recurrent neural network (RNN) is definitely a tool to consider. Part 1 will demonstrate some simple RNNs using TensorFlow 2.0 and Keras functional API. What is RNN An RNN is a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence (time series). This...

Convolutional Neural Network with CIFAR and Tensorflow (example)

Petamind A.I

Fig 1: A CNN sequence to classify handwritten digits (src: medium) In this article, we assume that you already understand the basic concepts of a convolutional neural network (CNN), e.g. one-hot coding, convolution, pooling, fully-connected layer, activation functions. If you are totally new to these terms, please find and read our other articles. The problem We will use Tensorflow to build a...

squared hinge loss

Petamind A.I

The squared hinge loss is a loss function used for “maximum margin” binary classification problems. Mathematically it is defined as: where ŷ the predicted value and y is either 1 or -1. Thus, the squared hinge loss is: 0* when the true and predicted labels are the same and* when ŷ≥ 1 (which is an indication that the classifier is sure that it’s the correct label)quadratically increasing with the...

Dealing with missing data

Petamind A.I

In real-world data, there are some instances where a particular element is absent because of various reasons, such as corrupt data, failure to load the information, or incomplete extraction. Handling the missing values is one of the greatest challenges faced by analysts because making the right decision on how to handle it generates robust data models. Let us look at different ways of imputing...

A.I, Data and Software Engineering

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