Intro to TensorFlow for Deep Learning
by Magnus Hyttsten · Google
Our Verdict
Worth it — with caveatsIntro to TensorFlow for Deep Learning (ud187) is a genuinely free, self-paced course built by the TensorFlow team at Google with Udacity, and for developers who want a fast, code-first on-ramp to deep learning it earns a recommendation. Across 11 short video lessons it walks you from a first Keras model on Fashion MNIST through CNNs, transfer learning, time-series forecasting, NLP, and on-device TensorFlow Lite, all delivered by Google engineers including Magnus Hyttsten and Developer Advocate Paige Bailey. Its real strength is accessibility and practicality: learners on Reddit and Class Central repeatedly praise it as a high-quality, no-cost introduction with Colab-based coding exercises, and it carries a 4.3/5 rating from 51 reviews on Udacity's own page. The honest trade-off is depth — it is an introduction, so the math and theory stay light, the exercise set is thin, and despite the catalog id it is NOT the TensorFlow Developer Certificate exam and grants no certificate of completion. Treat it as a strong free primer, not a credential or a rigorous deep-learning course.
Free, well-made, and ideal as a practical first exposure to TensorFlow/Keras for people who already code, but it is shallow on theory, light on hands-on assignments, and despite the 'cert' in the catalog id it issues no certificate and is not the paid TensorFlow Developer Certificate exam — so it is a 'take it for what it is' rather than an unconditional yes.
Best for: Software developers, students, and self-learners who can already write basic Python and want a quick, free, code-first introduction to deep learning with TensorFlow 2 and the Keras API — covering CNNs, transfer learning, NLP, and deploying models to mobile via TensorFlow Lite. Also a good warm-up before tackling a deeper course or before attempting the separate TensorFlow Developer Certificate.
Skip if: People who want a recognized certificate or credential (none is issued here), learners seeking rigorous mathematical foundations of deep learning, or those wanting a large, graded project/assignment set. Complete non-programmers will struggle since the course leans on reading and writing Python in Colab from early on, and anyone who needs PyTorch specifically should look elsewhere.
About This Course
Google and Udacity course covering TensorFlow basics, CNNs, transfer learning, and NLP through coding exercises.
What You'll Learn
Curriculum
Course orientation and how the lessons and Colab exercises are structured.
Core ML intuition and where deep learning fits, kept light on math.
Build, train, and evaluate a first neural network with Keras on the Fashion MNIST image dataset.
Convolutional neural network fundamentals for image classification.
Improving CNNs with techniques such as data augmentation and dropout.
Reusing pre-trained models to achieve strong accuracy with limited data.
Persisting, exporting, and reloading trained TensorFlow/Keras models.
Applying deep learning to sequential, time-dependent data.
Preparing text data with tokenization and learning word embeddings.
Using RNNs for natural language processing tasks.
Converting and deploying models to mobile and embedded/edge devices.
Prerequisites
- Basic Python programming (the coding exercises run in Google Colab from the first model onward)
- Ability to communicate fluently in written and spoken English (officially stated requirement)
- No prior machine learning or deep learning experience required
- No local GPU strictly needed — exercises run in Colab, though a GPU helps for any heavier experimentation
Instructor
Magnus Hyttsten
Instructor · Google
Pros & Cons
Pros
- Completely free with no paid tier, and built directly by the TensorFlow team at Google for credibility and up-to-date practice
- Code-first and practical: hands-on Colab exercises from the first lesson, taught by Google engineers (Magnus Hyttsten, Juan Delgado, Paige Bailey)
- Broad introductory coverage in one place — Keras basics, CNNs, transfer learning, NLP/RNNs, time series, and TensorFlow Lite deployment
- Self-paced and accessible; widely recommended on Reddit/Class Central as a quality free entry point and ranks highly among free AI courses
Cons
- Shallow on theory and math by design — it is an introduction, so it gives intuition rather than rigorous foundations
- Thin assignment/exercise set; several reviewers note a lack of substantial graded practice or projects
- No certificate of completion is issued, and despite the 'cert' in this catalog entry it is NOT the (separate, paid) TensorFlow Developer Certificate exam
- Some learner content has aged relative to the latest TensorFlow/Keras APIs, so minor code adjustments can be needed
Alternatives To Consider
Frequently Asked Questions
Is Intro to TensorFlow for Deep Learning free?
Yes — Intro to TensorFlow for Deep Learning is free to access. 100% free on Udacity with no paid upsell required to access the lessons or Colab exercises. Note: it does not grant a certificate. The TensorFlow Developer Certificate is a separate, paid (~$100) exam run by Google/TensorFlow, and Udacity's paid Nanodegrees are unrelated to this free course.
Who is Intro to TensorFlow for Deep Learning for?
Software developers, students, and self-learners who can already write basic Python and want a quick, free, code-first introduction to deep learning with TensorFlow 2 and the Keras API — covering CNNs, transfer learning, NLP, and deploying models to mobile via TensorFlow Lite. Also a good warm-up before tackling a deeper course or before attempting the separate TensorFlow Developer Certificate.
What will you learn in Intro to TensorFlow for Deep Learning?
Build and train your first neural network with the Keras API on the Fashion MNIST dataset; Design convolutional neural networks (CNNs) for image classification and improve them with augmentation; Apply transfer learning using pre-trained models to get strong results on small datasets; Save, load, and export trained models for reuse.
What are the prerequisites for Intro to TensorFlow for Deep Learning?
Basic Python programming (the coding exercises run in Google Colab from the first model onward); Ability to communicate fluently in written and spoken English (officially stated requirement); No prior machine learning or deep learning experience required; No local GPU strictly needed — exercises run in Colab, though a GPU helps for any heavier experimentation.
Is Intro to TensorFlow for Deep Learning worth it?
Free, well-made, and ideal as a practical first exposure to TensorFlow/Keras for people who already code, but it is shallow on theory, light on hands-on assignments, and despite the 'cert' in the catalog id it issues no certificate and is not the paid TensorFlow Developer Certificate exam — so it is a 'take it for what it is' rather than an unconditional yes.
How we reviewed this course
This is an independent editorial assessment by Cursarium, based on Google'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.