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CloudaQube

Ai Ml · advanced

Deep Learning with PyTorch

Master deep learning from first principles using PyTorch 2.x. You will build and train neural networks from scratch, implement convolutional and recurrent architectures, apply attention mechanisms, and fine-tune pretrained models for real-world tasks. Every concept is grounded in working Python code and backed by the theory from leading university curricula.

  • 11 hours
  • 28 lessons
  • 6 modules
  • updated April 2026
Module 1 freeNo credit cardFull course from $8.99/mo
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Try it before you sign up

Read a full lesson and answer 3 questions from the Module 1 quiz. No account needed.

free lesson · read · 35 min

Tensors: The Foundation of PyTorch

A tensor is a generalisation of scalars, vectors, and matrices to arbitrary dimensions. A 0-D tensor is a scalar, a 1-D tensor is a vector, a 2-D tensor is a matrix, and so on. PyTorch tensors are the central data…
question 1 of 3 · from the Module 1 quiz
Which attribute on a tensor holds the accumulated gradient after calling .backward()?
Pick an answer.
curriculum

6 modules, 28 lessons

Module 1 is free — lessons, hands-on walkthrough and quiz.

  • readTensors: The Foundation of PyTorch35 min
  • videoVideo: PyTorch Tensors: The Data Structure Behind Deep Learning13 min
  • videoVideo: The Fundamentals of Autograd14 min
  • hands-onAutograd and Computation Graphs40 min
  • quizTensors & Autograd — Knowledge Check20 min
how it's built

Built by the CloudaQube team

No celebrity instructor — here is exactly what goes into the course instead.

  • 01 6 guided hands-on walkthroughs you run in a real environment.
  • 02 6 module quizzes — every answer comes with an explanation.
  • 03 Last updated April 2026.
  • 04 Any module can become a hands-on lab in Labs Studio.
outcomes

What you'll be able to do

  • 01 Understand tensors, autograd, and PyTorch computation graphs
  • 02 Build custom neural networks with nn.Module
  • 03 Implement full training and validation pipelines with DataLoader
  • 04 Design and train CNNs for image classification including ResNet
  • 05 Understand RNNs, LSTMs, vanishing gradients, and self-attention
  • 06 Fine-tune pretrained torchvision models for custom datasets
  • 07 Save, load, and deploy PyTorch models
before you start
  • — Python proficiency (functions, classes, list comprehensions)
  • — NumPy familiarity
  • — Basic calculus (derivatives, chain rule)
  • — Basic linear algebra (matrices, dot products)
  • — Completion of an introductory machine learning course recommended
questions

Before you start

How long does it take?

About 11 hours of lessons, videos and hands-on walkthroughs across 6 modules.

What’s free?

Module 1 in full — lessons, hands-on walkthrough and quiz — with no card required. Any paid plan (from $8.99/mo) unlocks all 6 modules, and starts with a 14-day free trial.

get started

Start the course with Module 1 free.

No card required. Unlock every module from $8.99/mo when you’re ready.

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      Deep Learning with PyTorch — Online Course | CloudaQube