Learn TensorFlow and Deep Learning Fundamentals with Python - Code-First Introduction Part 1-2
via YouTube
Overview
Syllabus
- Intro/hello/how to approach this video.
- MODULE 0 START (TensorFlow/deep learning fundamentals).
- [Keynote] 1. What is deep learning?.
- [Keynote] 2. Why use deep learning?.
- [Keynote] 3. What are neural networks?.
- [Keynote] 4. What is deep learning actually used for?.
- [Keynote] 5. What is and why use TensorFlow?.
- [Keynote] 6. What is a tensor?.
- [Keynote] 7. What we're going to cover.
- [Keynote] 8. How to approach this course.
- 9. Creating our first tensors with TensorFlow.
- 10. Creating tensors with tf Variable.
- 11. Creating random tensors.
- 12. Shuffling the order of tensors.
- 13. Creating tensors from NumPy arrays.
- 14. Getting information from our tensors.
- 15. Indexing and expanding tensors.
- 16. Manipulating tensors with basic operations.
- 17. Matrix multiplication part 1.
- 18. Matrix multiplication part 2.
- 19. Matrix multiplication part 3.
- 20. Changing the datatype of tensors.
- 21. Aggregating tensors.
- 22. Tensor troubleshooting.
- 23. Find the positional min and max of a tensor.
- 24. Squeezing a tensor.
- 25. One-hot encoding tensors.
- 26. Trying out more tensor math operations.
- 27. Using TensorFlow with NumPy.
- MODULE 1 START (neural network regression).
- [Keynote] 28. Intro to neural network regression with TensorFlow.
- [Keynote] 29. Inputs and outputs of a regression model.
- [Keynote] 30. Architecture of a neural network regression model.
- 31. Creating sample regression data.
- 32. Steps in modelling with TensorFlow.
- 33. Steps in improving a model part 1.
- 34. Steps in improving a model part 2.
- 35. Steps in improving a model part 3.
- 36. Evaluating a model part 1 ("visualize, visualize, visualize").
- 37. Evaluating a model part 2 (the 3 datasets).
- 38. Evaluating a model part 3 (model summary).
- 39. Evaluating a model part 4 (visualizing layers).
- 40. Evaluating a model part 5 (visualizing predictions).
- 41. Evaluating a model part 6 (regression evaluation metrics).
- 42. Evaluating a regression model part 7 (MAE).
- 43. Evaluating a regression model part 8 (MSE).
- 44. Modelling experiments part 1 (start with a simple model).
- 45. Modelling experiments part 2 (increasing complexity).
- 46. Comparing and tracking experiments.
- 47. Saving a model.
- 48. Loading a saved model.
- 49. Saving and downloading files from Google Colab.
- 50. Putting together what we've learned 1 (preparing a dataset).
- 51. Putting together what we've learned 2 (building a regression model).
- 52. Putting together what we've learned 3 (improving our regression model).
- [Code] 53. Preprocessing data 1 (concepts).
- [Code] 54. Preprocessing data 2 (normalizing data).
- [Code] 55. Preprocessing data 3 (fitting a model on normalized data).
- MODULE 2 START (neural network classification).
- [Keynote] 56. Introduction to neural network classification with TensorFlow.
- [Keynote] 57. Classification inputs and outputs.
- [Keynote] 58. Classification input and output tensor shapes.
- [Keynote] 59. Typical architecture of a classification model.
- 60. Creating and viewing classification data to model.
- 61. Checking the input and output shapes of our classification data.
- 62. Building a not very good classification model.
- 63. Trying to improve our not very good classification model.
- 64. Creating a function to visualize our model's not so good predictions.
- 65. Making our poor classification model work for a regression dataset.
Taught by
Daniel Bourke