Engineer Neural Networks with PyTorch and Transformers
Learn to build production-ready neural networks with PyTorch, including finetuning transformers, in this hands-on path.
Skill level
IntermediateTime to complete
Average based on combined completion rates — individual pacing in lessons, projects, and quizzes may vary8 hoursProjects
3Prerequisites
2 coursesWe suggest you complete the following courses before you get started with Engineer Neural Networks with PyTorch and Transformers:- Intro to PyTorch and Neural Networks
- Intermediate Python 3
About this skill path
Neural networks power everything from search engines to self-driving cars — and demand for engineers who can build them is growing fast. In this Skill Path, you’ll learn to engineer neural networks using PyTorch, working with architectures like MLPs, CNNs, RNNs, and transformers. You’ll apply pretrained models like BERT, GPT, and CLIP to solve real-world problems in computer vision, NLP, and multimodal AI. Through hands-on practice, you’ll finetune transformer models using Hugging Face libraries, profile performance metrics, debug training issues, and learn to select the right architecture for different AI challenges. By the end, you’ll have the practical skills to build, optimize, and deploy production-ready AI systems.
Skills you'll gain
- Build neural networks using PyTorch, including MLPs, CNNs, RNNs, and transformers
- Apply pretrained models like BERT, GPT, and CLIP for computer vision, NLP, and multimodal tasks
- Finetune transformer models using Hugging Face libraries such as PEFT and bitsandbytes
Syllabus
6 units • 7 lessons • 3 projects • 6 quizzes- 1
Welcome to the Engineer Neural Networks Skill Path
Welcome to the Engineer Neural Networks Skill Path!
- 2
Neural Network Architectures
Learn neural network architectures with PyTorch to build deep learning models for image, text, and sequential data tasks.
- 3
Introduction to AI Transformer
Learn about what transformers are (the T of GPT) and how to work with them using Hugging Face libraries
- 4
Finetuning Transformer Models
Master the art of LLM finetuning with LoRA, QLoRA, and Hugging Face. Learn how to prepare, train and optimize models for specific tasks efficiently.
- 5
Engineer Neural Networks Portfolio Project: Intent Classification
Demonstrate your ability to build an end-to-end AI engineering project for an NLP text classification task by finetuning a transformer-based model with LoRA.
- 6
Engineer Neural Networks Skill Path Next Steps
Engineer Neural Networks Skill Path Next Steps!
Certificate of completion available with Plus or Pro
Earn a certificate of completion and showcase your accomplishment on your resume or LinkedIn.
Projects in this skill path
- 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. - practice Project
Exploring Transformers Lab: Exploring their Carbon Footprint
Begin to grasp the environmental impact of transformer models by experimenting with decoders and the CodeCarbon emissions tracking library.
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Reviews from learners
- The progress I have made since starting to use codecademy is immense! I can study for short periods or long periods at my own convenience - mostly late in the evenings.ChrisCodecademy Learner @ USA
- I felt like I learned months in a week. I love how Codecademy uses learning by practice and gives great challenges to help the learner to understand a new concept and subject.RodrigoCodecademy Learner @ UK
- Brilliant learning experience. Very interactive. Literally a game changer if you're learning on your own.John-AndrewCodecademy Learner @ USA
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Related resources
- Article
How to Use Hugging Face: Beginner's Guide to AI Models
Learn Hugging Face fundamentals to train transformer models, tokenize text, and deploy AI with Google Colab. Complete beginner tutorial. - Article
How to Fine Tune Large Language Models (LLMs)
Learn how to fine tune large language models (LLMs) in Python with step-by-step examples, techniques, and best practices. - Article
How do Vision Transformers Work? Architecture Explained
Learn how vision transformers (ViTs) work, their architecture, advantages, limitations, and how they compare to CNNs.
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What's included in skill paths
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.