You’ve made it to part 2 of the longest code-first learn TensorFlow and deep learning fundamentals video series on RU-vid!
This part continues right where part one left off so get that Google Colab window open and get ready to write plenty more TensorFlow code.
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Timestamps:
0:00 - Intro/hello/have you watched part 1? If not, you should
0:55 - 66. Non-linearity part 1 (straight lines and non-straight lines)
10:33 - 67. Non-linearity part 2 (building our first neural network with a non-linear activation function)
16:21 - 68. Non-linearity part 3 (upgrading our non-linear model with more layers)
26:40 - 69. Non-linearity part 4 (modelling our non-linear data)
35:18 - 70. Non-linearity part 5 (reproducing our non-linear functions from scratch)
49:45 - 71. Getting great results in less time by tweaking the learning rate
1:04:32 - 72. Using the history object to plot a model’s loss curves
1:10:43 - 73. Using callbacks to find a model’s ideal learning rate
1:28:16 - 74. Training and evaluating a model with an ideal learning rate
1:37:37 - [Keynote] 75. Introducing more classification methods
1:43:41 - 76. Finding the accuracy of our model
1:47:59 - 77. Creating our first confusion matrix
1:56:27 - 78. Making our confusion matrix prettier
2:10:28 - 79. Multi-class classification part 1 (preparing data)
2:21:04 - 80. Multi-class classification part 2 (becoming one with the data)
2:28:13 - 81. Multi-class classification part 3 (building a multi-class model)
2:43:52 - 82. Multi-class classification part 4 (improving our multi-class model)
2:56:35 - 83. Multi-class classification part 5 (normalised vs non-normalised)
3:00:48 - 84. Multi-class classification part 6 (finding the ideal learning rate)
3:11:27 - 85. Multi-class classification part 7 (evaluating our model)
3:25:34 - 86. Multi-class classification part 8 (creating a confusion matrix)
3:30:00 - 87. Multi-class classification part 9 (visualising random samples)
3:40:42 - 88. What patterns is our model learning?
#tensorflow #deeplearning #machinelearning
18 май 2024
