The traditional way to teach deep learning is bottom up. Linear algebra, then calculus, then optimisation, then a single neuron, then a network, and somewhere around week nine you classify some handwritten digits. It is intellectually honest and it has a serious problem, which is that most people quit in week four. They are doing mathematics with no evidence it leads anywhere they care about, and motivation is a finite resource.
Howard's argument, which he has made loudly for years, is that this ordering is a mistake inherited from how mathematicians prefer to organise knowledge rather than from how people actually learn. This book is that argument in practice. Chapter one trains a working image classifier at a genuinely good accuracy in a handful of lines. You do not know how any of it works.
You do know that it works, that you did it, and that the thing you were told requires a doctorate took an afternoon. Then the book spends the rest of its length taking that apart, layer by layer, until by the end you have implemented the pieces yourself. Whether this ordering suits you is the single most important question about the book. For experienced programmers it usually does, because it is how most of us learned to code in the first place.
You made something work, then you found out why. Being handed the theory of compilers before writing a program would have been absurd. Howard is asking why deep learning should be different, and for a lot of people the answer is that it should not. The writing has a quality I value, which is that it is opinionated.
Howard tells you what he thinks works, which techniques he considers overrated, what he would do in your situation. Compare this with the average technical book that carefully presents six options with balanced pros and cons and leaves you no better able to choose. Specific advice can be wrong and it is at least actionable, and Howard's track record on competitive results earns him the right to be direct. The material on practical problems is where it beats the textbooks.
How to build a dataset when you do not have one, which is most real projects. How much data you actually need, which is usually far less than people assume once transfer learning is in play. What to do when your model works in a notebook and fails on real inputs. How to choose a learning rate using the finder rather than by superstition.
These are the things that determine whether a project works and they are absent from the theoretically respectable material. The ethics chapter is genuinely good and I want to note it, because most books either skip this or include something perfunctory. This one engages with feedback loops, with dataset bias as a measurable and addressable property rather than an abstraction, and with the fact that the person deploying a model is responsible for its effects. It is substantive.
Now the concern that everyone raises, which is the fastai library. It is a high level wrapper over PyTorch with strong defaults, and it lets you do a great deal with very little code. The worry is that you learn the wrapper rather than the field, and end up unable to work in an environment that does not use it. That worry is partly answered by the book itself, because the later chapters rebuild the abstractions from scratch and by the end you have written a training loop, an optimiser and a basic autograd.
Somebody who finishes the whole thing genuinely understands what is underneath. The problem is that many readers do not finish, and someone who stops at chapter ten has real capability with fastai and shallow understanding of anything else. Since industry runs on PyTorch directly, or increasingly on higher level training frameworks that are not this one, the practical advice is to treat this as the first of two resources rather than the only one. The top down order also leaves gaps, and this is inherent rather than a flaw in execution.
Learning in this sequence means you accumulate things you are using without understanding, and the book fills most of them in later. Which ones you never return to depends entirely on your discipline, and readers who are honest with themselves usually find a few. A bottom up course leaves you with less capability and fewer holes. Neither is strictly better and you should know which failure mode you are choosing.
The age is the most serious practical issue now. This is from 2020. Computer vision and the fundamentals hold up well, and the transformer era does not. Generative models, large language models, diffusion, the entire shape of what people currently want to build with deep learning arrived after publication.
The free course has continued to evolve and the book has not, so treat the book as foundations and the course site as the current material. Finally, the accessibility claim is oversold. Howard says you need a year of coding experience, and that is accurate and easy to miss under the friendly framing. This moves quickly, expects comfort with Python, and is not a first programming resource.
My 4.3 is for a book with a genuinely different and largely correct pedagogical theory, executed with skill and given away free. It loses ground for library dependence that a rushed reader will not escape, and for missing the era that most people now want to learn about.