Learn TensorFlow and Deep Learning fundamentals with Python (code-first introduction) Part 1/2
Ready to learn the fundamentals of TensorFlow and deep learning with Python? Well, you’ve come to the right place.
After this two-part code-first introduction, you’ll have written 100s of lines of TensorFlow code and have hands-on experience with two important problems in machine learning: regression (predicting a number) and classification (predicting if something is one thing or another).
Open a Google Colab (if you’re not sure what this is, you’ll find out soon) window and get ready to code along.
Sign up for the full course - https://dbourke.link/ZTMTFcourse
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Chapters (19)
Intro/hello/how to approach this video
1:50
MODULE 0 START (TensorFlow/deep learning fundamentals)
1:53
[Keynote] 1. What is deep learning?
6:31
[Keynote] 2. Why use deep learning?
16:10
[Keynote] 3. What are neural networks?
26:33
[Keynote] 4. What is deep learning actually used for?
35:10
[Keynote] 5. What is and why use TensorFlow?
43:05
[Keynote] 6. What is a tensor?
46:40
[Keynote] 7. What we're going to cover
51:12
[Keynote] 8. How to approach this course
56:45
9. Creating our first tensors with TensorFlow
1:15:32
10. Creating tensors with tf Variable
1:22:40
11. Creating random tensors
1:32:20
12. Shuffling the order of tensors
1:42:00
13. Creating tensors from NumPy arrays
1:53:57
14. Getting information from our tensors
2:05:52
15. Indexing and expanding tensors
2:18:27
16. Manipulating tensors with basic operations
2:24:00
17. Matrix multiplication part 1
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