PyTorch Essential Training: Deep Learning
by Jonathan Fernandes · LinkedIn Learning
Our Verdict
Worth it — with caveatsTake it only as a fast, low-cost orientation to PyTorch: as of the current LinkedIn Learning page the course 'PyTorch Essential Training: Deep Learning' is a 1-hour-21-minute beginner-to-intermediate primer taught by Terezija Semenski (released April 15, 2024), holding a verified 4.4/5 from 386 ratings on the official page. It walks through PyTorch's value proposition, Google Colab setup, tensors (CPU/GPU, attributes, data types, random sampling), tensor manipulation including autograd, and a single end-to-end training pipeline (data loading, transforms, batching, model training, validation), reinforced by interactive CoderPad code challenges. Note an accuracy caveat: this catalog entry lists the older instructor (Jonathan Fernandes) and advertises CNNs and transfer learning, but the live course is the rebuilt Semenski version and does NOT cover convolutional networks or transfer learning. Treat it as a tensors-and-training-loop foundation, not a path to building production computer-vision models.
It is a solid, inexpensive 1.5-hour foundation in PyTorch tensors and the basic training loop with hands-on CoderPad practice, but it is short and stops well before CNNs, transfer learning, or any real project, so it only makes sense as a stepping stone for people who already pay for LinkedIn Learning or want a quick orientation before a deeper course.
Best for: Python-comfortable learners who already know basic machine-learning concepts and want a quick, structured first exposure to PyTorch tensors, autograd, and the standard train/validate loop; existing LinkedIn Learning subscribers; and people who learn better with short videos plus in-browser CoderPad coding challenges before committing to a longer course.
Skip if: Complete programming beginners, anyone expecting to build CNNs, computer-vision, transfer-learning, NLP, or transformer models (none are taught), people who want a substantial capstone or portfolio project, and self-learners on a strict budget who can get equivalent or deeper PyTorch foundations free from fast.ai or freeCodeCamp.
About This Course
Build deep learning models with PyTorch covering tensors, autograd, CNNs, and transfer learning for real-world projects.
What You'll Learn
Curriculum
Course intro 'Deep learning with PyTorch', 'What you should know' prerequisites, and a tour of the CoderPad coding environment.
Introduction to deep learning, why to use PyTorch, and Google Colab basics including free GPU.
Introduction to tensors, creating tensors via CPU and GPU examples, and moving tensors between CPUs and GPUs.
Different creation methods, tensor attributes, data types, creating tensors from random samples and like other tensors, plus a guided solution exercise.
Tensor operations, mathematical functions, linear-algebra operations, automatic differentiation (autograd), and a split-tensors solution exercise.
End-to-end training process: data preparation, loading, transforms, batching, model development and training, and validation/testing.
Next steps and suggestions for continued learning.
Prerequisites
- Working Python proficiency (functions, classes, basic OOP)
- Familiarity with core machine-learning / deep-learning concepts (the course's 'What you should know' assumes foundational knowledge)
- A Google account to use the free Google Colab GPU environment
- No local install required; coding is done in Colab and CoderPad in the browser
Instructor
Jonathan Fernandes
Instructor · LinkedIn Learning
Pros & Cons
Pros
- Very time-efficient: a complete, structured PyTorch foundation in about 1 hour 21 minutes
- Hands-on CoderPad code challenges with real-time feedback, plus runnable Google Colab notebooks (no local setup needed)
- Clear, well-sequenced coverage of the genuinely essential basics: tensors, GPU usage, autograd, and a full train/validate loop
- Solid verified reception (4.4/5 from 386 ratings on the official page) and taught by an instructor who has reached roughly 40,000 learners
- Comes with a shareable LinkedIn Certificate of Completion, useful directly on a LinkedIn profile
Cons
- Narrow and shallow scope: no CNNs, transfer learning, NLP/transformers, or deployment despite what the catalog entry implies
- Only one toy training pipeline and no substantial capstone or portfolio-grade project
- Locked behind a paid LinkedIn Learning subscription, while comparable or deeper PyTorch foundations are available free elsewhere
- Catalog/listing metadata is out of date (wrong instructor and overstated topics), so buyer expectations can be misaligned with the current content
Alternatives To Consider
Frequently Asked Questions
Is PyTorch Essential Training: Deep Learning free?
PyTorch Essential Training: Deep Learning is $29.99/mo. Requires a LinkedIn Learning subscription (about $29.99/month, or included with LinkedIn Premium Career); LinkedIn Learning typically offers a ~1-month free trial through which the course can be completed at no cost. There is no standalone one-time purchase and no permanent free audit.
Who is PyTorch Essential Training: Deep Learning for?
Python-comfortable learners who already know basic machine-learning concepts and want a quick, structured first exposure to PyTorch tensors, autograd, and the standard train/validate loop; existing LinkedIn Learning subscribers; and people who learn better with short videos plus in-browser CoderPad coding challenges before committing to a longer course.
What will you learn in PyTorch Essential Training: Deep Learning?
Why PyTorch is used for deep learning and how it compares as a framework; Setting up and working in Google Colaboratory, including free GPU access; Creating and inspecting tensors on CPU and GPU, with attributes, data types, and random sampling; Moving tensors between CPU and GPU devices.
What are the prerequisites for PyTorch Essential Training: Deep Learning?
Working Python proficiency (functions, classes, basic OOP); Familiarity with core machine-learning / deep-learning concepts (the course's 'What you should know' assumes foundational knowledge); A Google account to use the free Google Colab GPU environment; No local install required; coding is done in Colab and CoderPad in the browser.
Is PyTorch Essential Training: Deep Learning worth it?
It is a solid, inexpensive 1.5-hour foundation in PyTorch tensors and the basic training loop with hands-on CoderPad practice, but it is short and stops well before CNNs, transfer learning, or any real project, so it only makes sense as a stepping stone for people who already pay for LinkedIn Learning or want a quick orientation before a deeper course.
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on LinkedIn Learning's published course materials and aggregated public learner feedback (last reviewed 2026-06). We have not independently completed the course. Links to providers are standard references, not paid placements.
Sources
- Official LinkedIn Learning course page (instructor Terezija Semenski, 1h 21m duration, released 4/15/2024, 4.4/5 from 386 ratings, full chapter list)
- Johns Hopkins University 'Imagine' course listing (independently confirms course title and current instructor Terezija Semenski, links to LinkedIn Learning)
- Official LinkedInLearning GitHub repo for the course (Semenski, Colab notebooks; no CNN/transfer-learning content)
- Class Central listing for the course (provider, instructor, ratings overview)
- GitHub repo of the prior Jonathan Fernandes version (documents the older curriculum the catalog metadata reflects)