Youtor
Learn TensorFlow and Deep Learning fundamentals with Python codefirst introduction Part 22
YOUR LINK HERE:
http://youtube.com/watch?v=ZUKz4125WNI
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/tens... • Ask a question - https://github.com/mrdbourke/tensorfl... • See part 1 - • Learn TensorFlow and Deep Learning fu... • TensorFlow Python documentation - https://www.tensorflow.org/api_docs/p... • Connect elsewhere: • Web - https://www.mrdbourke.com • Livestreams on Twitch - / 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 (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
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