Artificial Neural Networks for Structured Data Classification

Artificial Neural Networks (ANN) is a machine learning model inspired by the biological neural networks in the human brain. It consists of interconnected nodes, called neurons, organized in multiple layers. In the context of structured data classification, ANN is trained to learn patterns and relationships within the input data to make predictions or classify new instances.

The input layer receives the structured data, which is usually represented as a vector or a matrix. Each neuron in the subsequent hidden layers performs a weighted sum of the inputs and passes it through an activation function to produce an output. The output layer provides the final prediction or classification result.

Training an ANN involves determining the optimal weights and biases for each neuron, typically through an optimization algorithm such as stochastic gradient descent. The model is trained by minimizing a specific cost or loss function, which measures the disparity between the predicted outputs and the actual labels of the training data.

1. Image Classification: ANNs have been widely applied to tasks such as object recognition, character recognition, and image segmentation in fields like computer vision and medical imaging.
2. Text Classification: ANNs can be used for sentiment analysis, spam detection, topic classification, and language identification, among other tasks in natural language processing (NLP).
3. Financial Fraud Detection: ANNs can be utilized to identify patterns and anomalies in financial data to detect fraudulent activities, such as credit card fraud or money laundering.

• TensorFlow: TensorFlow is an open-source library for deep learning that provides comprehensive support for building and training ANNs. It offers a high-level API, Keras, which simplifies the implementation of ANN models. Link to TensorFlow
• PyTorch: PyTorch is another popular deep learning framework with extensive support for building ANN models. It provides dynamic computational graphs, making it suitable for research and prototyping. Link to PyTorch
• scikit-learn: scikit-learn is a versatile machine learning library that also includes functionality for building simple ANN models. It provides an easy-to-use interface and various tools for data preprocessing, model selection, and evaluation. Link to scikit-learn

1. Sebastian Raschka: A renowned author and researcher in the field of machine learning, particularly deep learning. Sebastian has extensive expertise in implementing ANN models for structured data.
2. Francois Chollet: The creator of Keras, a popular high-level deep learning API for TensorFlow. Francois has significant contributions to the development and practical implementation of ANN models.
3. Andrew Ng: A prominent figure in the field of artificial intelligence and deep learning. Andrew has expertise in various machine learning techniques, including ANN, and several successful course offerings on the subject.
4. Jason Brownlee: An author and practitioner focused on applied machine learning. Jason has published numerous articles and books on deep learning and provides practical guidance on implementing ANN models.
5. Yann LeCun: A world-renowned researcher and one of the pioneers of deep learning. Yann's expertise includes ANN models for structured data, with significant contributions to the field of computer vision.

Please note that the expertise of individuals may evolve over time, and it's worth exploring their most recent work and publications.