Neural Networks Training Course

Course Overview

Learning Objectives

• Explain how artificial neurons, weights, biases and activation functions combine to produce a network's output.
• Build multilayer neural networks and understand how depth and width affect what they can learn.
• Select appropriate loss functions for regression and classification problems.
• Apply gradient descent and backpropagation to train a neural network.
• Organize training, validation and test data to measure true model performance.
• Detect overfitting and underfitting and apply regularization techniques to control them.
• Tune hyperparameters such as learning rate, batch size and network size in a systematic way.
• Evaluate neural networks with suitable metrics and interpret their predictions.
• Diagnose common training problems, including vanishing gradients and unstable learning.
• Build, train and evaluate a complete neural network for a business prediction task.

Who Should Attend

Learning Outcomes

• By the end of this course, participants will be able to:
• Describe the structure of a perceptron and a multilayer network and the role of each component.
• Compare common activation functions and choose suitable ones for hidden and output layers.
• Calculate and interpret loss for regression and classification tasks.
• Explain step by step how backpropagation computes gradients and updates weights.
• Train a multilayer network using gradient descent and its mini-batch and stochastic variants.
• Read learning curves to judge whether a model is learning, underfitting or overfitting.
• Apply dropout, weight penalties and early stopping to improve generalization.
• Tune hyperparameters using validation data and cross-validation.
• Evaluate a trained network with appropriate metrics and explain which inputs drive its predictions.
• Deliver an end-to-end neural network solution, from data preparation to an evaluated model.

Course Outline