Neural Networks for Big Money. Александр Чичулин

or z-score standardization) to prevent any particular feature from dominating the learning process.

      – One-Hot Encoding: Convert categorical variables into binary vectors (0s and 1s) to represent them numerically. This allows neural networks to process categorical data effectively.

      – Text Preprocessing: If working with text data, perform text preprocessing steps such as tokenization, stop word removal, stemming or lemmatization, and vectorization techniques (e.g., TF-IDF, word embeddings) to represent text data in a format suitable for neural networks.

      – Time Series Preprocessing: If dealing with time series data, handle tasks such as resampling, windowing, or lagging to transform the data into a format that captures temporal dependencies.

      7. Data Splitting: Split the preprocessed data into training, validation, and testing sets. The training set is used to train the neural network, the validation set is used for hyperparameter tuning and model selection, and the testing set is used to evaluate the final model’s performance. Consider appropriate ratios (e.g., 70-15-15) depending on the size of the dataset and the complexity of the problem.

      8. Data Augmentation (if applicable): In certain cases, data augmentation techniques can be used to artificially increase the

      size and diversity of the training data. This is especially useful in image or audio processing tasks, where techniques like image flipping, rotation, cropping, or audio perturbation can be applied to expand the dataset and improve the model’s generalization.

      9. Data Pipeline: Set up an efficient data pipeline to handle data loading, preprocessing, and feeding the data into the neural network during training and evaluation. Consider using libraries or frameworks that provide convenient tools for data pipeline management.

      10. Data Documentation: Maintain clear documentation of the data collection process, preprocessing steps, and any modifications made to the original data. This documentation helps ensure reproducibility and allows others to understand the data processing pipeline.

      By following these steps, you can collect and preprocess your data effectively, ensuring its quality, relevance, and suitability for training neural networks. Well-prepared data forms a strong foundation for building accurate and high-performing models that can help you achieve big money with neural networks.