Machine learning has been adopted for many applications, including natural language processing, pattern recognition, speech recognition, image recognition, and many more. Machine learning has revolutionized the way we live our lives and conduct business by providing a means through which enterprises unearth hidden patterns, trends, and insights from data to enhance processes. Deep learning, a subset of machine learning, has been a more efficient technique for making sense out of massive volumes of data thanks to the multiple processing layers present within its structure. Thanks to deep learning technology, speech recognition and image error rates have been slashed by 30% and 3.5%, respectively, to improve prediction and decision-making. This is a huge milestone and just two examples of the many successes of deep learning technology advancement.
While developing deep learning models requires much larger volumes of data, the accuracy of their outcome will be determined greatly by the quality of the training datasets. In the event that the unsupervised learning approach will be employed, it may be a bit more costly to produce high-quality datasets, but the results will certainly be more accurate. While this article looks at the broader types of deep learning datasets, there is so much more that one can learn about deep learning. Start free courses like the Free Deep Learning with Keras course as you advance your knowledge and skills with hands-on projects and practice exercises.
What is deep learning?
Deep learning is a branch of machine learning that mimics the human brain in learning and making decisions. Deep learning algorithms are designed with multiple layers of neural networks that learn progressively from data by extracting higher-level features from input datasets, almost similar to the working of the human brain. For this reason, deep learning requires vast amounts of data for training. The more accurate deep learning algorithms require more data because they use more parameters.
The advantage of deep learning is that both structured and unstructured data can be used as input data and it is more accurate depending on the quality of data used. Deep learning algorithms are designed to process data from multiple sources in real time without human intervention. However, once they have been trained, deep learning algorithms can only handle the specific problem that they were developed and trained for. Solving other problems may require retraining the models or building others from scratch.
What is a dataset?
A dataset is a collection of data or values about a particular topic that is often represented in an organized manner. Datasets can be organized in a tabular format where each column in a table represents a specific variable, depending on the problem being addressed. Datasets are useful for training algorithms that discover hidden trends and patterns from data to make accurate predictions.
Characteristics of datasets
The general characteristics of a dataset are:
- Dimensionality. The dimensionality of a dataset refers to the number of attributes of the objects in a dataset. The curse of dimensionality refers to a situation in which a dataset has too many attributes which complicate the analysis process. In other words, the more the number of attributes in a dataset, the more complex its analysis becomes.
- Sparsity. When most of the attributes of an object in a dataset have a value of zero, the dataset is referred to as a sparse dataset.
- Resolution. Resolution refers to the visibility of patterns within a dataset. A finer resolution means that patterns will not be seen easily. Also, patterns may be hidden by the noise in the data.
Types of datasets in deep learning
As we had seen earlier on, deep learning algorithms are built and trained using datasets. In deep learning, you will come across three main types of datasets.
- The training dataset
- Validation dataset
- Test dataset
Ideally, data is divided into the three types of datasets that will be used for specific purposes at different stages of deep learning algorithm development.
1.Training Data Set
The training dataset is used to train the deep learning model. It is used in the first stage of model development, in which data is fed into the deep learning algorithm to develop the model. The model will be exposed to input data examples where it will detect patterns to create and define the input parameters to be used in modeling data to deliver the desired output. The training dataset will use a huge percentage, at least 60% of the data, as this is the most crucial stage of model development that determines the accuracy of the model.
2.Validation dataset
The validation dataset is used to evaluate the model that has been created in the training phase. In principle, a data model should be evaluated using a different sample from the one that was used to train it. This will help to evaluate not only its predictive performance but also to identify and adjust the model hyperparameters and losses yielded by the model before it is validated. The validation data set typically takes up 20% of the data.
3.Testing dataset
The testing dataset is used for testing the models to understand how the model will work and gauge its accuracy. The importance of this phase is to check the quality of the model output and its accuracy. For this reason, the testing dataset, usually containing about 20% of the data, will contain the input parameters along with verified output.
The model testing stage is the final stage before the deep learning model is used in real-world situations. No adjustment is done to the model beyond this point. It is only expected that it will learn progressively from input samples to become more accurate at solving problems.
It is advisable to expose the model to the testing dataset only after the training face is complete. Model testing is the final measure of a model after fixes and adjustments have affected the model during validation.
The qualities of a good deep learning dataset
A good-quality dataset for deep learning will achieve the expected output. Always ensure that your dataset is:
- Relevant. The data used in any of your datasets, whether training, validation, or testing, should be relevant to the problem you will be addressing in the real world. Ideally, it should possess similar values or parameters to the data you will be using in the real world.
- The right quantity. Accurate deep learning models are typically built and trained from vast volumes of data. Thus, it is important to gather enough data for training your models. However, note that even with vast volumes of data, failure to clean and process it properly compromises its quality. Eventually, this, together with overtraining, could lead to overfitting your model.
- Properly classified. Deep learning techniques can be used on any category of data, including images, text, audio, video, transaction, and time series data. For this reason, raw data should be labeled to allow for building and training accurate models.
- Properly formatted. In addition to properly classifying your data, ensure that you vectorize it to have effective neural networks. Vectorizing your data will involve formatting your data to have uniform attributes, a process that is usually done during data pre-processing. The best approach is to prepare a list of the required features beforehand and then format your data accordingly.
Deep learning has proved useful in many ways in different sectors. For instance, it has been used widely to analyze massive social media data when creating targeted ads, predicting illnesses and pandemics in healthcare, predicting stock values in finance, and detecting advanced online security threats and vulnerabilities in systems. Deep learning datasets make it possible to organize large volumes of raw data into different categories based on their uses in the neural networks’ development cycle.