Learn TensorFlow and Deep Learning fundamentals with Python (code-first introduction) Part 2/2
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Published 2021-03-16
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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Get all of the code/materials on GitHub - www.github.com/mrdbourke/tensorflow-deep-learning/
Ask a question - github.com/mrdbourke/tensorflow-deep-learning/disc…
See part 1 - • Learn TensorFlow and Deep Learning fu...
TensorFlow Python documentation - www.tensorflow.org/api_docs/python/tf
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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
All Comments (21)
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The best part about Daniel's teaching is that he doesn't make his students feel like they are alone in their learning and becomes their companion by showing them how he would himself figure things out. This gives tremendous hope and confidence especially to the students who are just starting out in this field which is vast and ever-growing. Great job mate. You rock!!!
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It's been an amazing tensorflow learning for the last two weeks to code along part 1 and 2 tutorial videos with ease. Thank you so much Daniel
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Hey Daniel, I couldn't thank you enough for getting me started with TF. I am so grateful for all the work you put into the creation of your videos. Maybe, hopefully, someday I'll get to pay it forward. Thank you!
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Just wanted to say thank you for working hard on providing us with all these deep and informative videos on TensorFlow and Deep Learning.
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I am gonna be totally honest with you, these were the most productive 14 hours of my life, Thanks Daniel for being an excellent teacher : )
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Can't thank you enough Daniel for these amazing 2 videos, coded along with you and learnt so much... massive respect
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Great instructor, beautifully put together. I went on and registered for the full course in ZTM and I am watching everything from the beginning again.
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Just finished the 14 hours course ! Thankyou for making such a awesome tutorial Daniel.
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I have just completed this course. It's just amazing. I luckily found this amazing content on youtube which I really need as I am going to start my final year project. Thanks for all this
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Thank you Daniel, it was an amazing series of lectures and I am really grateful to you for teaching us not only how to work with DL, but also how to think when working with DL. It was one of the most influential lectures of my career.
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Thank you so much Daniel. This is actually the best course I had on TensorFlow. It was very helpful so I am very thankful for the hard work and the big effort you put to make it that successful.
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Thank you Daniel. The course syllabus is great but most importantly the way you teach is amazing and your voice is so sweet and balanced. It is like step by step guidance and every machine learning beginners must watch this series.
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Just finished the 2nd part thank you Daniel for this wonderful set of videos really appreciate your content
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After two weeks, I finally finished the whole course! It was a great tutorial Daniel, keep up the good work!!
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i have come this all way loving the course, and i will be coming back again after finishing the whole thing ... thank you mister daniel.
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One of the best (if not THE...) courses I found on youtube. I'm definitely gonna take the whole course. Well done! and thanks man for this great material 🙏🏾
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Thank you so much, Daniel. I just finished the 14 hours course for a month with 1 hour daily.
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Can't give you enough thanks sir, I will remember these two videos for a long time because I start my deep learning journey from here. And I think now I have a good habit to write code as much as I can rather than copying. Thank you very much sir for your effort. Love from Kolkata(India).
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This is absolutely fantastic. Can't tell you how valuable it has been for me.
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This series is the business! Thanks Dan looking forward to more of this!
