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- 76. Finding the accuracy of our model
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Classroom Contents
Learn TensorFlow and Deep Learning Fundamentals with Python - Code-First Introduction Part 2/2
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- 1 - Intro/hello/have you watched part 1? If not, you should
- 2 - 66. Non-linearity part 1 (straight lines and non-straight lines)
- 3 - 67. Non-linearity part 2 (building our first neural network with a non-linear activation function)
- 4 - 68. Non-linearity part 3 (upgrading our non-linear model with more layers)
- 5 - 69. Non-linearity part 4 (modelling our non-linear data)
- 6 - 70. Non-linearity part 5 (reproducing our non-linear functions from scratch)
- 7 - 71. Getting great results in less time by tweaking the learning rate
- 8 - 72. Using the history object to plot a model’s loss curves
- 9 - 73. Using callbacks to find a model’s ideal learning rate
- 10 - 74. Training and evaluating a model with an ideal learning rate
- 11 - [Keynote] 75. Introducing more classification methods
- 12 - 76. Finding the accuracy of our model
- 13 - 77. Creating our first confusion matrix
- 14 - 78. Making our confusion matrix prettier
- 15 - 79. Multi-class classification part 1 (preparing data)
- 16 - 80. Multi-class classification part 2 (becoming one with the data)
- 17 - 81. Multi-class classification part 3 (building a multi-class model)
- 18 - 82. Multi-class classification part 4 (improving our multi-class model)
- 19 - 83. Multi-class classification part 5 (normalised vs non-normalised)
- 20 - 84. Multi-class classification part 6 (finding the ideal learning rate)
- 21 - 85. Multi-class classification part 7 (evaluating our model)
- 22 - 86. Multi-class classification part 8 (creating a confusion matrix)
- 23 - 87. Multi-class classification part 9 (visualising random samples)
- 24 - 88. What patterns is our model learning?
