Deep Neural Networks with PyTorch

Now listed on Coursera as 'Introduction to Neural Networks and PyTorch' (the IBM course formerly titled 'Deep Neural Networks with PyTorch'), this is a hands-on, intermediate introduction to PyTorch fundamentals taught by IBM's Joseph Santarcangelo and the IBM Skills Network team. It holds a verified 4.4 out of 5 from 1,909 reviews on the official course page (about 87% of ratings are 4 or 5 stars), and roughly 100,800 learners have enrolled. Its real strength is the 'from scratch' pedagogy: you compute loss and gradient descent by hand before being shown PyTorch's built-in functions, which builds genuine intuition for tensors, autograd, and the training loop. The trade-offs are equally real, however: the current iteration centers on tensors, linear regression, and logistic regression rather than the deep/convolutional networks the older title and some catalog blurbs imply, narration is delivered by a text-to-speech voice at a flat pace, and reviewers cite easy labs plus occasional errors in slides and lab solutions. Treat it as a focused PyTorch on-ramp inside the IBM AI Engineering Professional Certificate, not a comprehensive deep-learning course.

Strong, well-sequenced PyTorch fundamentals with a build-it-from-scratch approach that genuinely teaches tensors, autograd, and the training loop, but the restructured course is narrower than its name suggests (heavy on linear/logistic regression, light on deep CNNs), uses synthetic text-to-speech narration, and has documented lab/slide errors. Worth it for the right learner; underwhelming for those expecting a full deep-learning curriculum.

Best for: Python programmers who want a structured, low-cost introduction to PyTorch's core mechanics (tensors, autograd, DataLoader, the optimizer/loss/backprop loop) and who like learning the math by implementing it manually. It is a natural fit for learners already pursuing the IBM AI Engineering Professional Certificate, and for people who know another framework (e.g., TensorFlow/Keras) and want PyTorch exposure or reinforcement of regression and gradient-descent concepts.

Skip if: Complete beginners with no Python, calculus, or matrix background (multiple reviewers note beginners struggle and lean on external help like ChatGPT); learners expecting a deep dive into convolutional networks, transformers, or large-scale model architectures; and anyone who needs polished human-recorded lectures, challenging graded coding assignments, or clear performance benchmarks on the final project (graded work is largely multiple-choice plus a peer-reviewed project with no accuracy rubric).