TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial
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Date: 2022-03-14
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Comments and reviews: 10
Academiclibrary
Learn how to use TensorFlow 2.0 in this full tutorial course for beginners. This course is designed for Python programmers looking to enhance their knowledge and skills in machine learning and artificial intelligence.
Throughout the 8 modules in this course you will learn about fundamental concepts and methods in ML & AI like core learning algorithms, deep learning with neural networks, computer vision with convolutional neural networks, natural language processing with recurrent neural networks, and reinforcement learning.
Each of these modules include in-depth explanations and a variety of different coding examples. After completing this course you will have a thorough knowledge of the core techniques in machine learning and AI and have the skills necessary to apply these techniques to your own data-sets and unique problems.
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Learn how to use TensorFlow 2.0 in this full tutorial course for beginners. This course is designed for Python programmers looking to enhance their knowledge and skills in machine learning and artificial intelligence.
Throughout the 8 modules in this course you will learn about fundamental concepts and methods in ML & AI like core learning algorithms, deep learning with neural networks, computer vision with convolutional neural networks, natural language processing with recurrent neural networks, and reinforcement learning.
Each of these modules include in-depth explanations and a variety of different coding examples. After completing this course you will have a thorough knowledge of the core techniques in machine learning and AI and have the skills necessary to apply these techniques to your own data-sets and unique problems.
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Aaron
I think this course gives a chance to anyone who wants to learn machine learning in a fast and free way.-
And save a bunch of time looking at papers and library literature.-
This course is gradual.-
There is a clear understanding of everything from linear regression to reinforcement learning, and even the example programs are fully described and annotated. The people who made and designed this course are very thoughtful and selfless sharing and deserve huge applause.-
Thank you very much.
reply
I think this course gives a chance to anyone who wants to learn machine learning in a fast and free way.-
And save a bunch of time looking at papers and library literature.-
This course is gradual.-
There is a clear understanding of everything from linear regression to reinforcement learning, and even the example programs are fully described and annotated. The people who made and designed this course are very thoughtful and selfless sharing and deserve huge applause.-
Thank you very much.
reply
Doru
Hi great course. However I noticed one explanation that is not correct regarding over fitting at time3:37:40 dnn example : over fitting vs. number of epochs. (you tried 10 then 8 then 1 epoch) . Actually that is not correct as each time you run , you continue from the last checkpoint . So first time you have epoch 0-10, then 10-18 and finally 18-19. Hope it helps!
reply
Hi great course. However I noticed one explanation that is not correct regarding over fitting at time3:37:40 dnn example : over fitting vs. number of epochs. (you tried 10 then 8 then 1 epoch) . Actually that is not correct as each time you run , you continue from the last checkpoint . So first time you have epoch 0-10, then 10-18 and finally 18-19. Hope it helps!
reply
Academiclibrary
Thanks for your great Course, followed the whole couse with taking notes and tweeking/playing with your .ipynb's. Has been over a week now since i started and finished Today with much greater understanding of TF and machine learning in general. I hope i can use this for my project. Big thanks for your time and effort man.
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Thanks for your great Course, followed the whole couse with taking notes and tweeking/playing with your .ipynb's. Has been over a week now since i started and finished Today with much greater understanding of TF and machine learning in general. I hope i can use this for my project. Big thanks for your time and effort man.
reply
itech
How to make a solution that can recognize a sound of a barking dog or even better the sound of a specific dog barking? Iphone and IPad can do that but can not forward the detection.. I would like to use a Raspberry Pi for it and look for an accurate and most simple solution. Thanks for any advise!!!
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How to make a solution that can recognize a sound of a barking dog or even better the sound of a specific dog barking? Iphone and IPad can do that but can not forward the detection.. I would like to use a Raspberry Pi for it and look for an accurate and most simple solution. Thanks for any advise!!!
reply
Manbir
I am on the 4th module ( Neural Networks ) and I have two things to say, you ( we all ) actually have bad handwriting ( in / on computer ) and second you explained everything really really well.
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I am on the 4th module ( Neural Networks ) and I have two things to say, you ( we all ) actually have bad handwriting ( in / on computer ) and second you explained everything really really well.
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WahranRai
5:17 tf.keras.layers.LSTM(32) is not equal to the size of the embedding word ! It is the size of output of LSTM.
You could have tf.keras.layers.LSTM(64) for example
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5:17 tf.keras.layers.LSTM(32) is not equal to the size of the embedding word ! It is the size of output of LSTM.
You could have tf.keras.layers.LSTM(64) for example
reply
Odunayo
Alright! This isi the most amazing tutorial I have seen with TensorFlow! I cant believe I can watch a tutorial for almost 7 hours being addicted. Thanks alot!
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Alright! This isi the most amazing tutorial I have seen with TensorFlow! I cant believe I can watch a tutorial for almost 7 hours being addicted. Thanks alot!
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guntherjw50
the agent at 28:13 is a pole dancer. Really enjoying the content brother! Great job. You have already destroyed any college professor I have experienced.
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the agent at 28:13 is a pole dancer. Really enjoying the content brother! Great job. You have already destroyed any college professor I have experienced.
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Saul
6:47:38 , dont update the Q table? which means we use pretrain table? dont write that line of code? Or you are saying something else. I am not sure what it mean
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6:47:38 , dont update the Q table? which means we use pretrain table? dont write that line of code? Or you are saying something else. I am not sure what it mean
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