Deep Learning Model Engineering and Optimization

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Deep Learning Model Engineering and Optimization

This course is part of Machine Learning Engineer: ML and Deep Learning Models Specialization

Instructor: Professionals from the Industry

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Select and justify DL architectures (MLP, CNN, Transformer) for a given problem and data.
Build, train, and evaluate a PyTorch baseline with clean training loops and metrics.
Optimize generalization via dropout, weight decay, LR schedules, optimizers, and tuning.

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Build your subject-matter expertise

This course is part of the Machine Learning Engineer: ML and Deep Learning Models Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

In Deep Learning Model Engineering and Optimization, you’ll learn to choose the right architecture, build a strong PyTorch baseline, and systematically optimize models for accuracy and generalization. This course is organized around real job tasks. You’ll start by checking what you already know, then focus on the skills you want to strengthen. If a topic is familiar, skip ahead; if it’s new, dive into targeted lessons curated from expert instructors so every minute builds a workplace skill.

Across task-based modules, you’ll practice selecting and justifying architectures (MLP, CNNs, Transformers) based on problem requirements; building and training baseline networks in PyTorch (nn.Module, nn.Sequential, training loops, evaluation); and improving models with regularization (dropout, L2 weight decay), hyperparameter tuning, weight initialization, optimizer choice (SGD, Adam), gradient clipping, and learning rate scheduling. Short, graded assessments help you confirm progress. By the end, you’ll be able to defend your design decisions to stakeholders, ship a working baseline, and iterate toward production-ready performance. These skills can help you prepare for roles like Deep Learning Engineer, Machine Learning Engineer, AI Engineer, Model Optimization Engineer, or Research Engineer, and handle the responsibilities you’ll see in real job descriptions.

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.

What's included

1 reading4 assignments

1 readingTotal 10 minutes

Start Here: How This Skill-Based Course Works10 minutes

4 assignmentsTotal 95 minutes

Skill Assessment Task 1: Architecture Selection and Justification30 minutes
Skill Assessment Task 2: Build and Train a Baseline Network30 minutes
Skill Assessment Task 3: Systematic Optimization and Generalization30 minutes
Skill Diagnostic: Find Your Recommended Starting Point5 minutes

Use this module to build the skills for the job task Architecture Selection and Justification. You’ll learn how to compare baseline and advanced models, evaluate tradeoffs beyond accuracy, and select the deep learning architecture that best fits a given problem and set of constraints. Review the lessons that match the skills you want to strengthen before completing the related graded assessment.

What's included

4 videos2 readings4 assignments1 ungraded lab

4 videosTotal 10 minutes

Choosing the Right Model Isn't Just About Accuracy2 minutes
Establishing a Baseline – Part 1: Training Simple Models2 minutes
Establishing a Baseline – Part 2: Evaluation and Model Selection2 minutes
Deep Learning for Vision and Text: CNNs and Transformers in Action3 minutes

2 readingsTotal 14 minutes

Choosing the Right Advanced Model for the Right Task7 minutes
Why Baselines Matter: Measuring Progress with Simple Models7 minutes

4 assignmentsTotal 90 minutes

Knowledge Check: Baseline Models & Metrics15 minutes
Knowledge Check: Advanced Modeling Techniques15 minutes
Model Selection & Implementation30 minutes
Practice Your Skills: Architecture Selection and Justification30 minutes

1 ungraded labTotal 60 minutes

Train an Advanced Model on Your Dataset60 minutes

Use this module to build the skills for the job task Build and Train a Baseline Network. You’ll learn how to construct a basic feedforward neural network in a deep learning framework, run the training loop, and evaluate performance using loss and accuracy signals. Review the lessons that match the skills you want to strengthen before completing the related graded assessment.

What's included

6 videos3 readings4 assignments3 ungraded labs

6 videosTotal 30 minutes

Building a Neural Network and Visualizing the Forward Pass8 minutes
Building the Perceptron Forward Pass in PyTorch6 minutes
Defining a Multi-Layer Perceptron with nn.Module and nn.Sequential7 minutes
Running a Forward Pass and Exploring Model Capacity2 minutes
Building the Training Loop for a Neural Network4 minutes
Evaluating Model Performance and Plotting Results4 minutes

3 readingsTotal 25 minutes

What Is Deep Learning and How Do Neural Networks Work?7 minutes
Understanding Loss Functions in Deep Learning8 minutes
Getting Started with Optimizers: How Models Learn10 minutes

4 assignmentsTotal 90 minutes

Knowledge Check - Foundations of Neural Networks15 minutes
Knowledge Check - Building and Training FNNs15 minutes
Mastering the Foundations of Deep Learning with PyTorch30 minutes
Practice Your Skills: Build and Train a Baseline Network30 minutes

3 ungraded labsTotal 180 minutes

Lab - Build and Visualize a Perceptron from Scratch60 minutes
Lab - Build Your Own Perceptron for Binary Classification60 minutes
Lab - Train an MLP for Handwritten Digit Classification60 minutes

Use this module to build the skills for the job task Systematic Optimization and Generalization. You’ll learn how to apply regularization techniques to reduce overfitting, improve training stability, and tune key hyperparameters to strengthen model performance and generalization. Review the lessons that match the skills you want to strengthen before completing the related graded assessment.

What's included

6 videos4 readings4 assignments3 ungraded labs

6 videosTotal 26 minutes

Training Deep Models Isn't Just About More Layers2 minutes
Applying Dropout to Prevent Overfitting7 minutes
Using L2 Regularization with Weight Decay3 minutes
Applying Custom Weight Initialization in PyTorch4 minutes
Choosing and Switching Optimizers in PyTorch7 minutes
Improving Stability: Gradient Clipping and Learning Rate Scheduling3 minutes

4 readingsTotal 38 minutes

What Is Overfitting & How Dropout and Weight Penalties Help10 minutes
L1/L2 in Practice and the Role of Batch Normalization10 minutes
Why Initialization and Optimizer Choice Matter10 minutes
Stabilizing Training with Gradient Clipping and Learning Rate Schedules8 minutes

4 assignmentsTotal 105 minutes

Knowledge Check - Regularization Techniques30 minutes
Knowledge Check - Initialization and Optimization15 minutes
Optimizing Deep Learning Models in PyTorch30 minutes
Practice Your Skills: Systematic Optimization and Generalization30 minutes

3 ungraded labsTotal 180 minutes

Lab - Experiment with Regularization Techniques for Neural Networks60 minutes
Lab - Experiment with Initialization and Optimizer Combinations60 minutes
Lab - Optimize Your Training Pipeline with Efficiency Tricks60 minutes

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.

What's included

2 readings

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Professionals from the Industry

489 Courses112,906 learners

Offered by

Coursera

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Frequently asked questions

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When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.