This curriculum is for people who are:
- New to ML, but who have an intermediate programming background
TensorFlow 2.0 is designed to make building neural networks for machine learning easy, which is why TensorFlow 2.0 uses an API called Keras. The book Deep Learning with Python by Francois Chollet, creator of Keras, is a great place to get started. Read chapters 1-4 to understand the fundamentals of ML from a programmer's perspective. The second half of the book delves into areas like Computer Vision, Natural Language Processing, Generative Deep Learning, and more. Don't worry if these topics are too advanced right now as they will make more sense in due time.
⬆ 或 ⬇
Take the TensorFlow Developer Specialization, which takes you beyond the basics into introductory Computer Vision, NLP, and Sequence modelling.
Completing this step continues your introduction, and teaches you how to use TensorFlow to build basic models for a variety of scenarios, including image classification, understanding sentiment in text, generative algorithms, and more.
Try some of our TensorFlow Core tutorials, which will allow you to practice the concepts you learned in steps 1 and 2. When you're done, try some of the more advanced exercises.
Completing this step will improve your understanding of the main concepts and scenarios you will encounter when building ML models.
步驟 4：更熟悉 TensorFlow
Now it's time to go back to Deep Learning with Python by Francois and finish chapters 5-9. You should also read the book Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, by Aurelien Geron. This book introduces ML and deep learning using TensorFlow 2.0.
Completing this step will round out your introductory knowledge of ML, including expanding the platform to meet your needs.