Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

In Custom Deep Learning Model Architecture, you’ll design, build, and optimize neural networks that solve real product problems. This is a skill-based, job‑task learning experience organized around the responsibilities you see in deep learning job descriptions. You’ll start with a quick skill check, then personalize your path: skip what you know, or dive into targeted lessons curated from expert instructors. In PyTorch, you’ll work with tensors and modules, assemble layers into perceptrons and MLPs, and write the training loop. You’ll build specialized models including CNNs for computer vision; RNNs, LSTMs, and GRUs for sequences; and generative models such as GANs, VAEs, and autoregressive networks for synthetic data. Finally, you’ll train and tune models using the right optimizers, dropout and L2 regularization, gradient clipping, and learning‑rate scheduling. By the end, you can design architectures, implement and debug custom models, and deliver production‑minded experiments. These skills help prepare you for roles like Deep Learning Engineer, Machine Learning Engineer, AI Engineer, Computer Vision Engineer, NLP Engineer, or modeling‑focused Data Scientist.

Syllabus

Start Here: Get Oriented and Check Your Skills

Start here to learn how this skill-based course works and find your recommended starting point. You’ll take a short, ungraded diagnostic to check your current skills, then decide whether to go directly to the graded skill assessments or review targeted learning content first.

Job Task 1: Foundations of Neural Network Design and Implementation

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.

Job Task 2: Build Specialized Deep Learning Architectures

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.

Job Task 3: Train and Optimize Custom Models

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.

Wrap Up: Review Your Skill Achievement and Choose Your Next Path

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.