Learn Neural Network Architectures
Learn neural network architectures with PyTorch to build deep learning models for image, text, and sequential data tasks.
Skill level
IntermediateTime to complete
Average based on combined completion rates — individual pacing in lessons, projects, and quizzes may vary2 hoursProjects
2Prerequisites
2 coursesWe suggest you complete the following courses before you get started with Learn Neural Network Architectures:- Learn Intermediate Python 3
- Intro to PyTorch and Neural Networks
About this course
Neural networks power modern AI applications, from image recognition to language processing. In this PyTorch deep learning tutorial, you’ll learn how to build and train multiple neural network architectures including MLPs, CNNs, RNNs, transformers like BERT and GPT, and multimodal models like CLIP. You’ll understand the design tradeoffs between different architectures, select the right model for specific AI problems, and implement them using PyTorch. By the end, you’ll be able to profile model performance, debug common training issues, and leverage pretrained models for real-world applications in computer vision, natural language processing, and multimodal AI.
Skills you'll gain
Build neural networks using PyTorch
Apply CNNs, RNNs, and transformers
Profile model performance metrics
Debug deep learning implementations
Syllabus
2 lessons • 2 projects • 2 quizzesCertificate of completion available with Plus or Pro
Earn a certificate of completion and showcase your accomplishment on your resume or LinkedIn.
Projects in this course
- practice Project
Build Neural Networks for Classifying Digits and Time-Series Predictions
Apply your knowledge of neural network architectures by building and training various models for classification and time series prediction tasks using popular real-world datasets. - practice Project
Classifying CIFAR-10 with Pretrained CLIP: Original vs Reconstructed Images
Explore how image reconstruction quality affects zero-shot classification performance of a pretrained CLIP model on CIFAR-10.
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Frequently asked questions about Neural Network Architectures
You should have basic Python programming experience and familiarity with fundamental machine learning concepts. Understanding of linear algebra and calculus is helpful but not required. We’ll introduce PyTorch from the ground up, so no prior deep learning framework experience is necessary.
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Looking for something else?
Related resources
- Article
Building a Neural Network using PyTorch
Learn how to build a PyTorch neural network step by step. This tutorial walks you through a complete PyTorch neural network example, covering model creation, training, and evaluation. - Article
Understanding Neural Networks and Their Components
Discover the neural network architecture and its core components to understand how they work. - Article
Long Short Term Memory Networks
Long short-term memory networks (LSTMs) are often used in deep learning programs for natural language processing.
Related courses and paths
- Learn how to use PyTorch to build, train, and test artificial neural networks in this course.
- Intermediate.3 hours
- Learn how to use Python to build text generation models based on neural networks like RNNs and LSTMs in this PyTorch tutorial.
- With Certificate
- Intermediate.3 hours
- Learn how to use PyTorch in Python to build text classification models using neural networks and fine-tuning transformer models.
- With Certificate
- Intermediate.4 hours
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Practice Projects
Guided projects that help you solidify the skills and concepts you're learning.Assessments
Auto-graded quizzes and immediate feedback help you reinforce your skills as you learn.Certificate of Completion
Earn a document to prove you've completed a course or path that you can share with your network.