Data preparation is a crucial step for deep learning, as it can affect the performance, accuracy, and efficiency of your models. In this article, you will learn some best practices and tips for prepping your data for deep learning, whether you are working with images, text, audio, or other types of data.
Before you start prepping your data, you need to understand its characteristics, such as its size, shape, distribution, quality, and relevance. You also need to define your problem and goal, such as what you want to predict, classify, or generate with your data. This will help you decide what type of data you need, how much data you need, and how to split your data into training, validation, and test sets.
Data preparation begins when we have well understood the problem statement. A clear problem statement will help to focus on gathering correct data and define quality measurement of data to qualify for training.
The first step is ensuring everyone understands that their output, no matter how insignificant they might think it is, is data in it’s raw form. Particularly focus on any output they repeat more than once. If something is done more than once, it should be automated. Keep track of use patterns - the more you produce something, the more quickly it should be automated. Automation will start with the high-volume use cases first and the best person to identify those are the people who do them. Not their managers, not their directors, or their VPs. Workers will lead initial process automation before systems are automated.
Cleaning and normalizing your data means removing or correcting any errors, outliers, missing values, duplicates, or inconsistencies in your data. This will improve the quality and reliability of your data, and reduce the noise and bias that can affect your models. You can use various techniques and tools, such as pandas, numpy, or scikit-learn, to clean and normalize your data. For example, you can use mean, median, or mode imputation to fill in missing values, or standardization or min-max scaling to normalize your data.
Many practitioners are very familiar with the concept of “garbage in, garbage out”. This holds true for everything under AI. I want to emphasise first on data bias - which is a complex issue. Flawed data can result into historical, representation and measurement bias in the training data. Diversity in training data has a major impact on whether a neural network is able to overcome bias or degrade its performance. An instance erroneous facial detection may arise if we just train the model on a certain subset class of data. Similarly, in NER, NLP use cases, techniques like stop word removal for common words, lemmatisation and stemming are important to reduce words to their root forms.
In my NLP tasks, cleaning and normalizing data is crucial. For example, I might remove any extra spaces, correct spelling mistakes, or turn all text to lowercase so that "APPLE" and "apple" are treated the same. Sometimes, sentences from the web come with HTML tags, which I'd strip off. Additionally, I might replace slang or shorthand with their full forms, like changing "u" to "you". Emojis can be a special challenge. For instance, if I come across "??", I might convert it to the word "smile" to capture its sentiment. This thorough tidying up makes sure that when I feed data to my model, it's getting the clearest, most consistent version of the text.
Transforming and augmenting your data means applying various operations or techniques to change or enhance your data, such as resizing, cropping, rotating, flipping, or adding noise to images, or tokenizing, stemming, lemmatizing, or embedding text. This will increase the diversity and richness of your data, and help your models learn more features and generalize better. You can use various libraries and frameworks, such as OpenCV, PIL, or TensorFlow, to transform and augment your data. For example, you can use image augmentation to create new images from existing ones, or word embeddings to represent text as vectors.
As you transform and re-train your data you will want to observe the time and complexity. If you are carrying a lot off loss, and not finer tuning your model overall with this transformed data, debug the data and validate the fastest functional way to provide the model data that it will like. The objective is to get your transformed data over to the origin model in a way that the model only parses and classifies elements it cares about. In short, reduce loss and gain more accuracy by finer tuning the data through transformations.
Encoding and formatting your data means converting your data into a suitable representation and structure for your models, such as numerical values, tensors, matrices, or arrays. This will make your data compatible and efficient for your models, and help your models interpret and process your data. You can use various methods and functions, such as one-hot encoding, label encoding, or encoding categorical features as numbers, to encode your data. For example, you can use one-hot encoding to represent classes or categories as binary vectors, or label encoding to assign numerical values to labels.
Batching and shuffling your data means dividing your data into smaller groups or batches, and randomly rearranging the order of your data or batches. This will improve the speed and memory usage of your models, and prevent overfitting and underfitting by exposing your models to different samples and patterns of your data. You can use various tools and methods, such as generators, iterators, or loaders, to batch and shuffle your data. For example, you can use a data loader to create batches of data and shuffle them before feeding them to your models.
Validating and testing your data means evaluating the quality and performance of your data and your models, such as checking for errors, anomalies, or inconsistencies in your data, or measuring the accuracy, precision, recall, or F1-score of your models. This will help you identify and fix any issues or problems with your data or your models, and optimize your results and outcomes. You can use various tools and methods, such as plots, graphs, statistics, or metrics, to validate and test your data. For example, you can use a confusion matrix to visualize the true and predicted labels of your data, or a ROC curve to compare the sensitivity and specificity of your models.
Ensure you assess your feature creation process for feature leakage! Feature leakage refers to features or remnants not present during prediction but exist in training, containing hidden target variable information. Common in time series data, it's like using "Monthly Salary" to predict "yearly salary." Always be critical about what features you introduce into your feature set and stay vigilant against leakage!
Ensuring data is unbiased Identify potential sources of bias within your data, like collection methods or historical factors. This awareness will help target bias during cleaning. Ensuring data collection process is diverse and representative, which minimizes sampling bias and produces a more comprehensive dataset. Implementing bias mitigation algorithms and methods to address bias within the data and machine learning models, ensuring fairness and accuracy.
Data Collection: Gather a diverse and representative dataset relevant to your task. Data Cleaning: Remove outliers, handle missing values, and normalize or standardize data for consistency. Data Splitting: Split the dataset into training, validation, and test sets to assess model performance accurately. Feature Engineering: Select, transform, or extract relevant features to represent the data effectively. Data Augmentation: Increase dataset size by applying transformations (e.g., rotations or flips) to training samples, enhancing model generalization.