Learn TensorFlow and Deep Learning Fundamentals with Python - Code-First Introduction Part 1-2

Learn TensorFlow and Deep Learning Fundamentals with Python - Code-First Introduction Part 1-2

Daniel Bourke via YouTube Direct link

- Intro/hello/how to approach this video

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1 of 69

- Intro/hello/how to approach this video

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Learn TensorFlow and Deep Learning Fundamentals with Python - Code-First Introduction Part 1-2

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  1. 1 - Intro/hello/how to approach this video
  2. 2 - MODULE 0 START (TensorFlow/deep learning fundamentals)
  3. 3 - [Keynote] 1. What is deep learning?
  4. 4 - [Keynote] 2. Why use deep learning?
  5. 5 - [Keynote] 3. What are neural networks?
  6. 6 - [Keynote] 4. What is deep learning actually used for?
  7. 7 - [Keynote] 5. What is and why use TensorFlow?
  8. 8 - [Keynote] 6. What is a tensor?
  9. 9 - [Keynote] 7. What we're going to cover
  10. 10 - [Keynote] 8. How to approach this course
  11. 11 - 9. Creating our first tensors with TensorFlow
  12. 12 - 10. Creating tensors with tf Variable
  13. 13 - 11. Creating random tensors
  14. 14 - 12. Shuffling the order of tensors
  15. 15 - 13. Creating tensors from NumPy arrays
  16. 16 - 14. Getting information from our tensors
  17. 17 - 15. Indexing and expanding tensors
  18. 18 - 16. Manipulating tensors with basic operations
  19. 19 - 17. Matrix multiplication part 1
  20. 20 - 18. Matrix multiplication part 2
  21. 21 - 19. Matrix multiplication part 3
  22. 22 - 20. Changing the datatype of tensors
  23. 23 - 21. Aggregating tensors
  24. 24 - 22. Tensor troubleshooting
  25. 25 - 23. Find the positional min and max of a tensor
  26. 26 - 24. Squeezing a tensor
  27. 27 - 25. One-hot encoding tensors
  28. 28 - 26. Trying out more tensor math operations
  29. 29 - 27. Using TensorFlow with NumPy
  30. 30 - MODULE 1 START (neural network regression)
  31. 31 - [Keynote] 28. Intro to neural network regression with TensorFlow
  32. 32 - [Keynote] 29. Inputs and outputs of a regression model
  33. 33 - [Keynote] 30. Architecture of a neural network regression model
  34. 34 - 31. Creating sample regression data
  35. 35 - 32. Steps in modelling with TensorFlow
  36. 36 - 33. Steps in improving a model part 1
  37. 37 - 34. Steps in improving a model part 2
  38. 38 - 35. Steps in improving a model part 3
  39. 39 - 36. Evaluating a model part 1 ("visualize, visualize, visualize")
  40. 40 - 37. Evaluating a model part 2 (the 3 datasets)
  41. 41 - 38. Evaluating a model part 3 (model summary)
  42. 42 - 39. Evaluating a model part 4 (visualizing layers)
  43. 43 - 40. Evaluating a model part 5 (visualizing predictions)
  44. 44 - 41. Evaluating a model part 6 (regression evaluation metrics)
  45. 45 - 42. Evaluating a regression model part 7 (MAE)
  46. 46 - 43. Evaluating a regression model part 8 (MSE)
  47. 47 - 44. Modelling experiments part 1 (start with a simple model)
  48. 48 - 45. Modelling experiments part 2 (increasing complexity)
  49. 49 - 46. Comparing and tracking experiments
  50. 50 - 47. Saving a model
  51. 51 - 48. Loading a saved model
  52. 52 - 49. Saving and downloading files from Google Colab
  53. 53 - 50. Putting together what we've learned 1 (preparing a dataset)
  54. 54 - 51. Putting together what we've learned 2 (building a regression model)
  55. 55 - 52. Putting together what we've learned 3 (improving our regression model)
  56. 56 - [Code] 53. Preprocessing data 1 (concepts)
  57. 57 - [Code] 54. Preprocessing data 2 (normalizing data)
  58. 58 - [Code] 55. Preprocessing data 3 (fitting a model on normalized data)
  59. 59 - MODULE 2 START (neural network classification)
  60. 60 - [Keynote] 56. Introduction to neural network classification with TensorFlow
  61. 61 - [Keynote] 57. Classification inputs and outputs
  62. 62 - [Keynote] 58. Classification input and output tensor shapes
  63. 63 - [Keynote] 59. Typical architecture of a classification model
  64. 64 - 60. Creating and viewing classification data to model
  65. 65 - 61. Checking the input and output shapes of our classification data
  66. 66 - 62. Building a not very good classification model
  67. 67 - 63. Trying to improve our not very good classification model
  68. 68 - 64. Creating a function to visualize our model's not so good predictions
  69. 69 - 65. Making our poor classification model work for a regression dataset

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