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
Get all of the code/materials on GitHub – https://www.github.com/mrdbourke/tensorflow-deep-learning/
Ask a question – https://github.com/mrdbourke/tensorflow-deep-learning/discussions
See part 2 – https://youtu.be/ZUKz4125WNI
TensorFlow Python documentation – https://www.tensorflow.org/api_docs/python/tf

Connect elsewhere:
Web – https://www.mrdbourke.com
Livestreams on Twitch – https://www.twitch.tv/mrdbourke
Get email updates on my work – https://www.mrdbourke.com/newsletter

Timestamps:
0:00 – 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
2:35:55 – 18. Matrix multiplication part 2
2:49:25 – 19. Matrix multiplication part 3
2:59:27 – 20. Changing the datatype of tensors
3:06:24 – 21. Aggregating tensors
3:16:14 – 22. Tensor troubleshooting
3:22:27 – 23. Find the positional min and max of a tensor
3:31:56 – 24. Squeezing a tensor
3:34:57 – 25. One-hot encoding tensors
3:40:44 – 26. Trying out more tensor math operations
3:45:31 – 27. Using TensorFlow with NumPy
3:51:14 – MODULE 1 START (neural network regression)
3:51:25 – [Keynote] 28. Intro to neural network regression with TensorFlow
3:58:57 – [Keynote] 29. Inputs and outputs of a regression model
4:07:55 – [Keynote] 30. Architecture of a neural network regression model
4:15:51 – 31. Creating sample regression data
4:28:39 – 32. Steps in modelling with TensorFlow
4:48:53 – 33. Steps in improving a model part 1
4:54:56 – 34. Steps in improving a model part 2
5:04:22 – 35. Steps in improving a model part 3
5:16:55 – 36. Evaluating a model part 1 (“visualize, visualize, visualize”)
5:24:20 – 37. Evaluating a model part 2 (the 3 datasets)
5:35:22 – 38. Evaluating a model part 3 (model summary)
5:52:39 – 39. Evaluating a model part 4 (visualizing layers)
5:59:56 – 40. Evaluating a model part 5 (visualizing predictions)
6:09:11 – 41. Evaluating a model part 6 (regression evaluation metrics)
6:17:19 – 42. Evaluating a regression model part 7 (MAE)
6:23:10 – 43. Evaluating a regression model part 8 (MSE)
6:26:29 – 44. Modelling experiments part 1 (start with a simple model)
6:40:19 – 45. Modelling experiments part 2 (increasing complexity)
6:51:49 – 46. Comparing and tracking experiments
7:02:08 – 47. Saving a model
7:11:32 – 48. Loading a saved model
7:21:49 – 49. Saving and downloading files from Google Colab
7:28:07 – 50. Putting together what we’ve learned 1 (preparing a dataset)
7:41:38 – 51. Putting together what we’ve learned 2 (building a regression model)
7:55:01 – 52. Putting together what we’ve learned 3 (improving our regression model)
8:10:45 – [Code] 53. Preprocessing data 1 (concepts)
8:20:21 – [Code] 54. Preprocessing data 2 (normalizing data)
8:31:17 – [Code] 55. Preprocessing data 3 (fitting a model on normalized data)
8:38:57 – MODULE 2 START (neural network classification)
8:39:07 – [Keynote] 56. Introduction to neural network classification with TensorFlow
8:47:31 – [Keynote] 57. Classification inputs and outputs
8:54:08 – [Keynote] 58. Classification input and output tensor shapes
9:00:31 – [Keynote] 59. Typical architecture of a classification model
9:10:08 – 60. Creating and viewing classification data to model
9:21:39 – 61. Checking the input and output shapes of our classification data
9:26:17 – 62. Building a not very good classification model
9:38:28 – 63. Trying to improve our not very good classification model
9:47:42 – 64. Creating a function to visualize our model’s not so good predictions
10:02:50 – 65. Making our poor classification model work for a regression dataset

#tensorflow #deeplearning #machinelearning

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