Why neural networks do more than classify?

Jarrell Hermann asked a question: Why neural networks do more than classify?
Asked By: Jarrell Hermann
Date created: Tue, Feb 9, 2021 1:22 PM

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Those who are looking for an answer to the question «Why neural networks do more than classify?» often ask the following questions:

💻 How do neural networks classify iris?

These steps show the summary of IRIS flower and neural network 1- Load the dataset and clean it to extract the feature vector. 2- Normalize the extracted features.

💻 How do neural networks classify non-linear data?

Unfortunately this activation function is exactly what allows the neural networks to solve the XOR problem or classify non-linearly segregated data. No activation function, and you take the NN’s power to solve non-linearity. Lets see it in more detail by trying to solve the XOR non-linearity with non activation function.

💻 How to classify two images without neural networks?

The CIFAR-1 0 dataset consists of 60000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. There are 50000 training images and 10000 test images.

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Currently, I am using a neural network to classify data into one of three groups (a logistic activation function is used on all but the output nodes). I can train the neural network in two ways: 1) For each observation $X_{i}$ I can have one output variable $O_{i}$ that can take three values: $1,2,$ or $3$.

Why neural network is important? What they are and why they matter. Neural networks are computing systems with interconnected nodes that work much like neurons in the human brain. Using algorithms, they can recognize hidden patterns and correlations in raw data, cluster and classify it, and – over time – continuously learn and improve.

I just started to learn about neural networks and so far my knowledge of machine learning is simply linear and logistic regression. from my understanding of the latter algorithms, is that given multiples of inputs the job of the learning algorithm is to come up with appropriate weights for each input so that eventually I have a polynomial that either describes the data which is the case of linear regression or separates it as in the case of logistic regression.

So, no matter how many layers or how many neurons we use, the way we proceeded so far, our neural network will still be just a linear classifier. We need something more. We need to take the weighted sum computed by each neuron and pass it through a non-linear function , then consider the output of this function as the output of that neuron.

Neural network models are increasingly relied upon for different problems due to the ease at which they can label or classify the input data. Different neural networks are trained with different hyperparameters, and then they are used to analyse the same validation training set.

Adjusting its weights: The network learns from its mistakes by adjusting its intrinsic structure depending on the insights gained by looking at the error function. This improves classification in the future. Lather, rinse, repeat: Run the training procedure long enough so that your network learns to classify the training data correctly. Test the quality of the classification on data that wasn’t used for training.

Then a network can learn how to combine those features and create thresholds/boundaries that can separate and classify any kind of data. Realistically, data is often a lot more complex than rainbow color data, but neural networks just layer separation on top of separation layer to create more complex boundaries and group all kinds of data.

If you want to do that using neural networks, the output of your system consists of the center of the detected item, the height and the width of the detected item, which is a regression task. The interpretation of outputs for regression tasks is like estimating a real value, this is why linear activation functions are used as the last layers' activation.

In particular, we find increased features sizes and more long-range dependencies the deeper the networks get. Hence, deeper neural networks do improve over simpler bag-of-feature models but I don’t think that the core classification strategy has really changed. Going beyond bag-of-features classification

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