You have manufactured it to portion 2 of the longest code-1st study TensorFlow and deep learning fundamentals video series on YouTube!
This portion continues proper in which portion 1 remaining off so get that Google Colab window open up and get completely ready to publish a great deal more TensorFlow code.
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See component 1 – https://youtu.be/tpCFfeUEGs8
TensorFlow Python documentation – https://www.tensorflow.org/api_docs/python/tf
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Timestamps:
:00 – Intro/hello/have you watched section 1? If not, you really should
:55 – 66. Non-linearity component 1 (straight traces and non-straight strains)
10:33 – 67. Non-linearity part 2 (building our first neural community with a non-linear activation purpose)
16:21 – 68. Non-linearity portion 3 (upgrading our non-linear product with extra layers)
26:40 – 69. Non-linearity section 4 (modelling our non-linear knowledge)
35:18 – 70. Non-linearity element 5 (reproducing our non-linear capabilities from scratch)
49:45 – 71. Receiving great success in considerably less time by tweaking the understanding rate
1:04:32 – 72. Using the history item to plot a model’s loss curves
1:10:43 – 73. Making use of callbacks to find a model’s perfect finding out fee
1:28:16 – 74. Teaching and assessing a design with an great learning fee
1:37:37 – [Keynote] 75. Introducing much more classification approaches
1:43:41 – 76. Locating the precision of our design
1:47:59 – 77. Building our initial confusion matrix
1:56:27 – 78. Generating our confusion matrix prettier
2:10:28 – 79. Multi-class classification aspect 1 (making ready info)
2:21:04 – 80. Multi-class classification part 2 (becoming just one with the knowledge)
2:28:13 – 81. Multi-class classification component 3 (setting up a multi-course model)
2:43:52 – 82. Multi-class classification section 4 (increasing our multi-course design)
2:56:35 – 83. Multi-class classification section 5 (normalised vs non-normalised)
3:00:48 – 84. Multi-course classification aspect 6 (locating the ideal understanding rate)
3:11:27 – 85. Multi-class classification aspect 7 (assessing our model)
3:25:34 – 86. Multi-class classification part 8 (producing a confusion matrix)
3:30:00 – 87. Multi-class classification component 9 (visualising random samples)
3:40:42 – 88. What patterns is our product discovering?
