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EDWartens UK

Free course · AI and machine learning

Deep Learning with PyTorch for Engineers

Deep learning in PyTorch on engineering data: tensors and autograd, nn.Module and a training loop you write yourself, Datasets and DataLoaders over sensor logs, CNNs and transfer learning for defect images, 1-D CNNs and LSTMs for vibration and process tags, experiment tracking, Hugging Face models, and export to ONNX for a line PC.

See the modules

Free to learn. Verifiable certificate £19 when you pass the final exam (optional, VAT included).

Level
Intermediate
Modules
13
Video
23 hours
Price
Free

Outcome

What you will be able to do

Build and train PyTorch models on plant data: create and move tensors, use autograd, write nn.Module networks and a full training loop with validation, early stopping and the right metrics; serve sensor windows and images through Datasets and DataLoaders without leakage; train CNNs and fine-tune torchvision models for defect images; classify and forecast signals with 1-D CNNs and LSTMs; track experiments reproducibly; use Hugging Face models; and export to ONNX with a parity and latency check.

  • Create, reshape and broadcast tensors, and move models and data between CPU, CUDA and Apple MPS
  • Work gradients by hand with the chain rule and check them with autograd
  • Write nn.Module networks and a full training loop with validation, early stopping and the best checkpoint
  • Serve sensor windows and images through Datasets and DataLoaders, split by time or machine without leakage
  • Train CNNs on defect images and fine-tune torchvision models with the weights API
  • Classify vibration with 1-D CNNs and forecast process tags with LSTMs, against a naive baseline
  • Make runs reproducible, track them, and use Hugging Face models as ordinary PyTorch modules
  • Export to ONNX and prove the file with a parity test and a latency budget for a line PC

Software

Python 3.10 or later with PyTorch, torchvision, ONNX Runtime and Hugging Face Transformers. Google Colab runs every exercise in a browser, with a free GPU when one is available, so nothing has to be installed.

Hardware

None beyond a computer with a browser. A local NVIDIA GPU or an Apple silicon Mac is useful but not required.

Syllabus

13 modules

Each module has video lessons, notes and a short quiz. The final exam closes the course.

  1. 1Tensors, devices and the PyTorch workflow3 lessons
  2. 2Autograd and gradient descent, by hand and in PyTorch5 lessons
  3. 3Your first network: regression on plant sensor data4 lessons
  4. 4Classification and the training loop done properly6 lessonsLab
  5. 5Datasets, DataLoaders and transforms for plant data3 lessonsLab
  6. 6Convolutional networks for defect images2 lessons
  7. 7Transfer learning with torchvision models4 lessons
  8. 81-D CNNs and LSTMs for vibration and time series5 lessons
  9. 9Experiment tracking and reproducibility4 lessons
  10. 10Hugging Face models from PyTorch3 lessons
  11. 11Export: saving, torch.export, ONNX and ONNX Runtime for line PCs3 lessons
  12. 12Project review: a bearing-fault classifier from data to ONNX1 lessonLab
  13. 13Final assessment0 lessons

How it works

How this course works

  1. Watch the video lessonsOne module at a time, at your own pace.
  2. Take the module quizA short quiz at the end of each module shows what stuck.
  3. Keep your notesWrite in My notes beside each module and ask the trainer if you are stuck.
  4. Practise in the labOptionalTry a task from the module in the EDWartens Lab, in your browser.
  5. Sit the final examPass it once you have been through the modules.
  6. Certificate, if you want oneOptionalPaid, and only after you pass. Anyone can check it online.

EDWartens Lab

Practise this in the EDWartens Lab

Train a model on plant data, move the decision threshold and weigh the cost of errors in the Lab's AI workbench.

Works alongside Deep Learning with PyTorch for Engineers. The Lab is ai for industry practice in your browser: no install, no hardware, free to use. Sign in as a learner and it opens with your account.

Open the free lab See the Lab
AI for industry: Build AI models.

Levels to try with this course

Optional. Skip it whenever you like: it never blocks your progress or your certificate.

Why practise in a lab

Technical work is judged on what you can do: wiring, commissioning, finding a fault. Doing a task yourself teaches more than watching it a second time, and a lab lets you try it safely before you meet real equipment. It does not replace working on real hardware.

  • Practise safely, so a mistake costs nothing.
  • The lab checks your work like a site test, so you know when it is right.
  • Free, in your browser, with nothing to install.

Certificate

An optional certificate when you pass

Learning is free. Pass the final exam and you can have a certificate for £19, including VAT, with a number employers can check online. It records that you completed a free online course; it is not a regulated qualification or a CPD certificate.

  • Your name, the course, its study time and your final exam score
  • A unique certificate number and a QR code to its verification page
  • Issued by Wartens Ltd; add it to your LinkedIn profile in one click
  • Pay now or after you pass: it is issued the moment you pass the final

How the certificate works

Sample certificate for Deep Learning with PyTorch for Engineers

Credits

Who made the lessons

The video lessons belong to their creators. EDWartens wrote the study plan, notes, quizzes and the final exam.

  • Patrick Loeber

    the PyTorch Tutorials series (tensors, autograd, backpropagation, the training pipeline, regression and classification, Dataset and DataLoader, transforms, softmax and cross entropy, activations, feed-forward and convolutional networks, transfer learning, TensorBoard, saving and loading, RNN, LSTM and GRU, the learning-rate scheduler and PyTorch Lightning) and the LSTM time-sequence forecasting tutorial

  • PyTorch

    the official Introduction to PyTorch and The Fundamentals of Autograd videos, and Philipp Schmid's talk on PyTorch 2.0 with Hugging Face Transformers

  • Jeff Heaton

    lectures from Applications of Deep Neural Networks with PyTorch: introductions to PyTorch and to networks, reading weights by hand, multi-class metrics with ROC and AUC, pretrained networks, hyperparameters and LSTM time series

  • Andrej Karpathy

    building micrograd, an autograd engine written from scratch

  • Daniel Bourke arXiv

    the long PyTorch transfer learning session

  • Aladdin Persson

    the PyTorch CNN example and the transfer learning and fine-tuning tutorial

  • StatQuest with Josh Starmer

    Long Short-Term Memory, clearly explained

  • PyData

    the talk on 1-D convolutional neural networks for time series by Nathan Janos and Jeff Roach

  • CoreWeave

    the Weights & Biases tutorial on integrating experiment tracking with PyTorch

  • Hugging Face

    what happens inside the pipeline function (PyTorch)

  • AssemblyAI

    getting started with Hugging Face transformers, pipelines, tokenizers and models

  • ONNX Runtime

    converting models to the ONNX format

  • Abhishek Thakur

    converting a PyTorch model to ONNX and serving it

  • Zachary Huang

    PyTorch in one hour, the recap in the project module

  • Fireship

    PyTorch in 100 seconds

Questions

About this course

Who is this PyTorch course for?
It is for engineers and final-year engineering students who know basic machine learning and want to build deep learning models themselves: instrumentation, electrical, mechanical and automation engineers working with sensor data, inspection images and plant tags. Data engineers in manufacturing will find it useful too.
Is the course free to learn?
Yes. Every module, the notes, the worked problems, the practice tasks, the project and the final assessment are free, and all the software is free. The certificate is optional.
What do I need to know before starting?
You should be able to write a Python function and use NumPy, and know what training and test sets are. Machine Learning with Python is the course before this one. The calculus needed, derivatives and the chain rule, is taught in module 2.
Do I need a GPU or any software installed?
No. Google Colab runs every exercise in a browser and offers a free GPU when one is available. If you want to work locally, install Python 3.10 or later and PyTorch from pytorch.org; an NVIDIA GPU or an Apple silicon Mac speeds training but is not required. PyTorch 2.13 was current in October 2026, so check the version you install.
How is this different from the TensorFlow deep learning course?
Deep Learning with TensorFlow and Keras teaches the ideas through Keras's built-in training. This course is about PyTorch itself: you write the training loop, the Dataset and the export, which is how most current research models, Ultralytics YOLO and Hugging Face models are built. The examples are sensor time series, defect images and tabular plant data, and the course ends with ONNX export for a line PC.
How long does the course take?
About 23 hours at your own pace, of which about 16 hours is video and the rest is notes and practice. The optional bearing-fault project takes about 16 hours more.
What certificate do I get?
An EDWartens certificate of completion, issued when you finish the modules and pass the 15-question final at 60 percent, with a number anyone can verify on our site. It is not a certification from the PyTorch Foundation, Meta, Hugging Face or any creator, none of whom is affiliated with EDWartens.
What jobs does PyTorch lead to?
PyTorch is the framework behind most vision, language and robot-learning models, so it is asked for in machine learning engineer, computer vision engineer and industrial AI roles at manufacturers, automation firms and engineering service companies. Go on to Machine Vision and Quality Inspection, Predictive Maintenance with Machine Learning or MLOps Model Deployment and Monitoring to aim at a specific role.

The Professional Module is five live days of Siemens PLC, HMI and SCADA with an instructor, then a resume session and a CPD certificate.

See the Professional Module