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Welcome to the Zero to Mastery TensorFlow for Deep Learning Book

Zero to Mastery TensorFlow for Deep Learning cover

This is the online book version of the Zero to Mastery Deep Learning with TensorFlow course.

This course will teach you foundations of deep learning and TensorFlow as well as prepare you to pass the TensorFlow Developer Certification exam (optional).

The course is video based. However, the videos are based on the contents of this online book.

For full code and resources see the course GitHub.

Course materials

The following table represents contents of the book (each notebook is a chapter) with extra links to slides, exercises and extra-curriculum.

Number Notebook Exercises & Extra-curriculum Slides
00 TensorFlow Fundamentals Go to exercises & extra-curriculum Go to slides
01 TensorFlow Regression Go to exercises & extra-curriculum Go to slides
02 TensorFlow Classification Go to exercises & extra-curriculum Go to slides
03 TensorFlow Computer Vision Go to exercises & extra-curriculum Go to slides
04 Transfer Learning Part 1: Feature extraction Go to exercises & extra-curriculum Go to slides
05 Transfer Learning Part 2: Fine-tuning Go to exercises & extra-curriculum Go to slides
06 Transfer Learning Part 3: Scaling up Go to exercises & extra-curriculum Go to slides
07 Milestone Project 1: Food Vision πŸ”πŸ‘, Template (your challenge) Go to exercises & extra-curriculum Go to slides
08 TensorFlow NLP Fundamentals Go to exercises & extra-curriculum Go to slides
09 Milestone Project 2: SkimLit πŸ“„πŸ”₯ Go to exercises & extra-curriculum Go to slides
10 TensorFlow Time Series Fundamentals & Milestone Project 3: BitPredict πŸ’°πŸ“ˆ Go to exercises & extra-curriculum Go to slides
11 Preparing to Pass the TensorFlow Developer Certification Exam Go to exercises & extra-curriculum Go to slides

Course structure

This course is code first. The goal is to get you writing deep learning code as soon as possible.

It is taught with the following mantra:

Code -> Concept -> Code -> Concept -> Code -> Concept

This means we write code first then step through the concepts behind it.

If you've got 6-months experience writing Python code and a willingness to learn (most important), you'll be able to do the course.

Should you do this course?

Do you have 1+ years experience with deep learning and writing TensorFlow code?

If yes, no you shouldn't, use your skills to build something.

If no, move onto the next question.

Have you done at least one beginner machine learning course and would like to learn about deep learning/pass the TensorFlow Developer Certification?

If yes, this course is for you.

If no, go and do a beginner machine learning course and if you decide you want to learn TensorFlow, this page will still be here.

Prerequisites

What do I need to know to go through this course?

  • 6+ months writing Python code. Can you write a Python function which accepts and uses parameters? That’s good enough. If you don’t know what that means, spend another month or two writing Python code and then come back here.
  • At least one beginner machine learning course. Are you familiar with the idea of training, validation and test sets? Do you know what supervised learning is? Have you used pandas, NumPy or Matplotlib before? If no to any of these, I’d going through at least one machine learning course which teaches these first and then coming back.
  • Comfortable using Google Colab/Jupyter Notebooks. This course uses Google Colab throughout. If you have never used Google Colab before, it works very similar to Jupyter Notebooks with a few extra features. If you’re not familiar with Google Colab notebooks, I’d suggest going through the Introduction to Google Colab notebook.
  • Plug: The Zero to Mastery beginner-friendly machine learning course (I also teach this) teaches all of the above (and this course, the one you're reading about now, is designed as a follow on).

How to use this book

All of the materials are taught code-first. The chapters are Jupyter Notebooks (also Google Colab notebooks) which can be run interactively.

You can read all of the materials but they'll be best learned if you practice writing the code yourself.

running a notebook chapter in Google Colab To start running a notebook interactively, click the "Open in Colab" button at the top of each chapter.

Who made this book?

I did, ah, me, Daniel, Daniel Bourke. I'm a machine learning engineer who makes YouTube videos and writes stories, pop philosophy and machine learning coding tutorials (like the ones contained in this book).

Sometimes documentaiton and other resources can be a bit hard to read for certain things. So I've done my best to make this a book (and a video course, I mean, that's where this book came from) I'd like to have read when I was getting into the exciting world of deep learning.

Extensions

Enjoyed this book/course?

I'd also recommend the following:

Get ready to dream in tensors!

Onward.