Deep Learning Model Engineering and Optimization

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Deep Learning Model Engineering and Optimization

Ce cours fait partie de Spécialisation "Machine Learning Engineer: ML and Deep Learning Models"

Instructeur : Professionals from the Industry

Inclus avec

5 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

5 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

2 semaines à compléter

à 10 heures par semaine

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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.

Compétences que vous acquerrez

Catégorie : Model Training
Catégorie : Machine Learning Methods
Catégorie : Network Model
Catégorie : Applied Machine Learning
Catégorie : Deep Learning
Catégorie : Model Optimization
Catégorie : Technical Communication
Catégorie : Performance Tuning
Catégorie : Artificial Neural Networks
Catégorie : Model Evaluation
Catégorie : Convolutional Neural Networks

Outils que vous découvrirez

Catégorie : Model Deployment
Catégorie : AI Workflows
Catégorie : PyTorch (Machine Learning Library)

Détails à connaître

Certificat partageable

Ajouter à votre profil LinkedIn

Récemment mis à jour !

juillet 2026

Évaluations

16 affectations¹

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Enseigné en Anglais

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Élaborez votre expertise du sujet

Ce cours fait partie de la Spécialisation "Machine Learning Engineer: ML and Deep Learning Models"

Lorsque vous vous inscrivez à ce cours, vous êtes également inscrit(e) à cette Spécialisation.

Apprenez de nouveaux concepts auprès d'experts du secteur
Acquérez une compréhension de base d'un sujet ou d'un outil
Développez des compétences professionnelles avec des projets pratiques
Obtenez un certificat professionnel partageable

Il y a 5 modules dans ce cours

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.

Inclus

1 lecture4 devoirs

1 lectureTotal 10 minutes

Start Here: How This Skill-Based Course Works10 minutes

4 devoirsTotal 95 minutes

Skill Diagnostic: Find Your Recommended Starting Point5 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

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.

Inclus

4 vidéos2 lectures4 devoirs1 laboratoire non noté

4 vidéosTotal 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 lecturesTotal 14 minutes

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

4 devoirsTotal 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 laboratoire non notéTotal 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.

Inclus

6 vidéos3 lectures4 devoirs3 laboratoires non notés

6 vidéosTotal 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 lecturesTotal 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 devoirsTotal 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 laboratoires non notésTotal 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.

Inclus

6 vidéos4 lectures4 devoirs3 laboratoires non notés

6 vidéosTotal 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 lecturesTotal 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 devoirsTotal 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 laboratoires non notésTotal 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.

Inclus

2 lectures

Obtenez un certificat professionnel

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Instructeur

Professionals from the Industry

489 Cours112 906 apprenants

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Foire Aux Questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

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.

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