COURSE OVERVIEW

This practical and highly-interactive course includes real-life case studies and exercises where participants will be engaged in a series of interactive small groups and class workshops. 

This course is designed to provide participants with a detailed and up-to-date overview of Artificial Intelligence Multilayer Perceptron. It covers the artificial intelligence, multilayer perceptron and the fundamentals of artificial (ANN); the multilayer perceptron (MLP), activation functions and forward propagation in MLP; the development environment, backpropagation, cost function and optimization; the hyperparameter tuning in MLP and the causes of overfitting in neural networks; and the regularization techniques, data augmentation to reduce overfitting and cross-validation strategies. 

 

Further, the course will also discuss the performance of training and evaluating an MLP model; implementing MLP in TensorFlow and Keras and the deep MLP architectures; selecting important selecting features for MLP, handling categorical variables in MLP and data preprocessing best practices; the MLP for classification tasks, regression tasks and real-world applications; and using MLP for time-series forecasting. 

During this interactive course, participants will learn the batch normalization and dropout for efficient training; optimizing MLP with transfer learning and implementing early stopping and model checkpoints; using TensorBoard for MLP training visualization; the parallel and distributed training for MLP and fine-tuning MLP models for optimal performance; deploying MLP models in production; the model explainability and interpretability and MLP in edge devices; the security and ethical considerations in AI; and the future of multilayer perceptrons in AI.