Learn TensorFlow and Deep Learning fundamentals with Python (code-first introduction) Part 2/2
You’ve made it to part 2 of the longest code-first 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.
Sign up for the full course - https://dbourke.link/ZTMTFcourse
Get all of the code/materials on GitHub - https://www.github.com/mrdbourke/tensorflow-deep-learning/
Ask a question - https://github.com/mrdbourke/tensorflow-deep-learning/discussions
See part 1 - https://youtu.be/tpCFfeUEGs8
TensorFlow Python documentation - https://www.tensorflow.org/api_docs/python/tf
Connect elsewhere:
Web - https://www.mrdbourke.com
Livestreams on Twitch - https://www.twitch.tv/mrdbourke
Get email updates on my work - https://www.mrdbourke.com/newsletter
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 (buil
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Chapters (17)
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
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 (buil
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