I want to separate two judgements here, because they point in opposite directions. Judged as a set of learning materials, this is better made than PyTorch's equivalent. Judged as a career decision, learning TensorFlow first in 2026 is probably a mistake. Both things are true and the review is basically an attempt to hold them together.
The craft is not in question. Google has serious technical writers and it shows on every page. The beginner quickstart gets you from nothing to a trained MNIST classifier in about fifteen lines, and the pacing of the early Keras tutorials is well judged. Basic image classification, basic text classification, regression, overfitting and underfitting, save and load.
Each one is a self contained notebook that runs in Colab with a free GPU behind it, with no environment setup, no CUDA versions, no dependency conflicts. For somebody who has never trained a model, that is still the single lowest friction on ramp that exists, and I have watched complete beginners get further in an afternoon with this than with anything else. The tf.data guides are genuinely good and cover something most courses skip. Input pipelines, prefetching, shuffling, handling data that does not fit in memory, TFRecord format.
Data loading is where a lot of real projects actually bottleneck and this material is more thorough than its PyTorch counterpart. The deployment story is the other clear advantage. TensorFlow Lite for mobile, TensorFlow.js for browsers, TensorFlow Serving for production, and the TPU coverage. If your problem is getting a model onto a phone or into a browser, the TensorFlow ecosystem is more mature and better documented than anything on the PyTorch side, and that has not changed.
The problem is everything else. Research moved to PyTorch and it moved decisively. New papers ship PyTorch code. Hugging Face is PyTorch first.
Most new job listings that name a framework name PyTorch. This is not a claim about which library is better engineered, it is an observation about where the ecosystem network effects landed, and network effects are what determine whether the code you find on GitHub runs. Learning TensorFlow now means a steady low level tax of finding that the implementation you wanted to read is in a framework you do not use. The second problem is inherited confusion.
TensorFlow has been through more architectural upheaval than any other major ML library. TF1 with sessions and graphs, TF2 with eager execution, the estimator API that came and went, tf.keras, and now Keras 3 which is multi backend and can run on PyTorch or JAX. The documentation carries sediment from all of it. Search for how to do something and you will find pages representing three different eras, not always labelled, and it takes experience to tell which is current.
That is a bad property in a resource aimed at learners, because the whole point is that they cannot yet tell. The third problem is the same one PyTorch's tutorials have. This teaches you an API. Every notebook uses a clean dataset, trains successfully, prints an accuracy and stops.
There is nothing about problem framing, nothing about whether your metric is the right one, nothing about diagnosing a model that has silently learned the wrong thing. The generative and LLM material also lags. There are tutorials on transformers and text generation, but the centre of that work happens elsewhere now and the coverage feels dutiful rather than current. So who should use this.
If you work somewhere with a TensorFlow codebase, obviously, and it will serve you well. If you are targeting mobile or browser deployment, yes, the tooling advantage is real. If you are deep in Google Cloud and Vertex AI, yes. If you want the Keras 3 experience, note that Chollet's book now covers that better and is backend agnostic anyway.
And if you are a total beginner who wants the fastest possible path to seeing a neural network work, the Colab integration makes a reasonable case for spending one afternoon here before switching. My 3.8 is not a comment on the quality of the writing, which would justify higher. It is a comment on the value of what you end up holding. Free, polished, accurate materials for a framework whose relevance is narrowing every year, with a documentation set carrying visible archaeological layers.
Good resource, wrong bet, unless you have a specific reason.