A.I, Data and Software Engineering

# Search results forneural

## Recurrent neural network – predict monthly milk production

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

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)

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...

## Lasso vs Ridge vs Elastic Net – Machine learning

Lasso, Ridge, and Elastic Net are excellent methods to improve the performance of your linear model. This post will summarise the usage of these regularization techniques. Bias: Biases are the underlying assumptions that are made by data to simplify the target function. Bias does help us generalize the data better and make the model less sensitive to single data points. It also decreases the...

## squared hinge loss

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...

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