You’ve made it to part 2 of the longest codefirst learn TensorFlow and deep learning fundamentals video series on YouTube!

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. Nonlinearity part 1 (straight lines and nonstraight lines)
10:33 67. Nonlinearity part 2 (building our first neural network with a nonlinear activation function)
16:21 68. Nonlinearity part 3 (upgrading our nonlinear model with more layers)
26:40 69. Nonlinearity part 4 (modelling our nonlinear data)
35:18 70. Nonlinearity part 5 (reproducing our nonlinear 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. Multiclass classification part 1 (preparing data)
2:21:04 80. Multiclass classification part 2 (becoming one with the data)
2:28:13 81. Multiclass classification part 3 (building a multiclass model)
2:43:52 82. Multiclass classification part 4 (improving our multiclass model)
2:56:35 83. Multiclass classification part 5 (normalised vs nonnormalised)
3:00:48 84. Multiclass classification part 6 (finding the ideal learning rate)
3:11:27 85. Multiclass classification part 7 (evaluating our model)
3:25:34 86. Multiclass classification part 8 (creating a confusion matrix)
3:30:00 87. Multiclass classification part 9 (visualising random samples)
3:40:42 88. What patterns is our model learning?

#tensorflow #deeplearning #machinelearning