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 - Get all of the code/materials on GitHub - Ask a question - See part 2 - TensorFlow Python documentation - Connect elsewhere: Web - Livestreams on Twitch - Get email updates on my work - 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