Use this module to build the skills for the job task Foundations of Neural Network Design and Implementation. You'll learn how to select and combine appropriate layer types when designing a neural network architecture, work with tensors and the PyTorch building blocks that underpin every neural network, and implement custom Artificial Neural Network architectures by building a perceptron, defining a multi-layer perceptron, and writing the training loop that brings the network to life. Review the lessons that match the skills you want to strengthen before completing the related graded assessment.

Use this module to build the skills for the job task Build Specialized Deep Learning Architectures. You'll learn how to apply Convolutional Neural Network (CNN) architectures for image and vision tasks, use Recurrent Neural Networks (RNNs), LSTMs, and GRUs to model sequential data such as time series and text, and apply generative models like GANs, VAEs, and autoregressive models to create synthetic data. Review the lessons that match the skills you want to strengthen before completing the related graded assessment.

Use this module to build the skills for the job task Train and Optimize Custom Models. You'll learn how to apply optimization algorithms to train and tune deep learning models, including using dropout and L2 regularization to prevent overfitting, choosing the right optimizer for the task, and applying gradient clipping and learning rate scheduling to stabilize training at scale. Review the lessons that match the skills you want to strengthen before completing the related graded assessment.

Review the skills you practiced and demonstrated in this course, then prepare to describe them in career-relevant ways. You’ll also explore recommended skill paths that can help you continue building related job-ready skills.