Deep learning is the subset of machine learning which is made up of neural networks. Neural Networks or Artificial Neural Networks (ANNs) are algorithms that recognize an underlying pattern in the data in a manner that our biological brain works. 

A neural network comprises the input layer, hidden layer, and output layer. Basically, the neural network with three or more hidden layers is referred to as a deep learning network.

Deep learning is different from machine learning. machine learning works on structured data with specific features pre-determined by the practitioner. 

On the other hand, deep learning can work on unstructured data without any predetermined features. This means the deep learning model learns the feature itself. This is also why a deep learning model requires a larger set of data to generalize the meaning from the dataset.

There are two most important things that a deep learning model requires. They are the amount of data and the computational resources. 

With a lot of cloud computing services being readily available nowadays, insufficient data seems to be the only problem for deep learning. There may not be proper availability of training data in every field; however, it is available in most. This has been the reason why deep learning is gaining popularity. 

With time, the amount of data will continuously grow, making training deep learning models even easier; on the other hand, it will increase their popularity even more. Training time, computational power, and the amount of data are always a problem when it comes to training a deep learning network. 

But, beyond that, there is a separate problem that needs to be addressed: the scaling problem. Real-world data is irregular, and thus, scaling the dataset is a primary task on a deep learning pipeline.

Using Relevant Data

In deep learning or machine learning, the model learns based on the training samples it experiences. The quantity and the quality of the training data determine how much the machine can learn. While training a model, it is always necessary to feed them with relevant data such that their predictions will also become relevant. 

Data is irrelevant when it consists of errors, outliers, and noise. Such data may also consist of missing observations. Techniques like data cleaning, winsorization, replacing values with mean and medians can be applied as a part of data pre-processing. 

There is one more issue that can exist, which is the data range. For example, consider a dataset about a community with one of the features as “salary.” If we consider a massive population with low and high-income households, the difference in salary between the poor and rich may be huge. 

This will naturally increase variance in the training result. Thus, it is necessary to scale the data to the same range using techniques like standardization and normalization.

Improving Model Accuracy

When the features are not scaled before training models, a wide difference exists among values. The higher values will dominate the smaller value, and they’ll have less of an impact on the model training. But, scaling the features to the same range, the training algorithm will be equally sensitive to all the samples and unbiased to the ones with greater magnitude. 

Because of this, the accuracy of the model will improve as the bias decreases, and the predictions will be closer to the target values. Methods like standardization and normalization bring the values to a range between 0 to 1 or -1 to 1. This range can also be modified to other ranges with requirements. These are the essential factors responsible for improving model accuracy.

The use of CPU and GPU

Deep learning requires a huge model, and training them is a time-consuming and difficult process. It is also already clear that deep learning makes use of huge computational resources involving CPUs and GPUs. 

The data that are unscaled takes a longer time to train and fit. But, scaling provides an advantage of faster training with comparatively lower computational resource consumption. PyTorch CPU guide will further help in optimizing the available CPU resources for scaling. 

The real-world data is irregular, with random differences in the data range. With the help of scaling, the data is brought to a specific range; thus, the training process is faster, and the model accuracy can be improved significantly. It further helps in maintaining the bias-variance trade-off.

Also Read: Everything You Need To Know About Google’s Deepmind AI Algorithm