This is my solution to sigmoid.m function in Programming assignment 2 from the famous Machine Learning course by Andrew Ng.


Github: https://github.com/AladdinPerz....on/Courses/tree/mast

In this video we will learn how to build a convolutional neural network (cnn) in TensorFlow 2.0 using the Keras Sequential and Functional API. We take a look at how to do max pooling layers, batch normalization etc.

Resources or prerequisite videos:
1. Deep learning specialization course 4 (No need to watch all, for example not after C4W2L02):
https://www.youtube.com/playli....st?list=PLkDaE6sCZn6

2. Batch Normalization video:
https://youtu.be/nUUqwaxLnWs

I learned a lot and was inspired to make these TensorFlow videos by the TensorFlow Specialization on Coursera. Below you'll find both affiliate and non-affiliate links, the pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
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TensorFlow Playlist:
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Enhancements:
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Enhanced lighting effects.
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Resolution 7680 x4320 pixels without youtube compression

So...after considerable amounts of rendering time - 8k for Youtube made with the GH5 begs only one question...does size matter? Here's a tutorial.
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YOP 2014
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Director Péter Engert
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Aadama Jaichomada (transl. We won without playing) is a 2014 Tamil language black comedy film directed by Badri. Produced by B&C films and Upshot Films, co-produced by Skilite Creations, the film stars Karunakaran, Bobby Simha and Vijayalakshmi in prominent roles, while Balaji Venugopal, Aadukalam Naren, K. S. Ravikumar amongst others appear in supporting roles. The dialogues were written by actor Shiva.[1] The film deals with corruption involved in cricket.[2] Aadama Jaichomada was released on 19 September 2014.

Cast
Karunakaran as Panneer
Bobby Simha as Inspector Bhoominathan
Vijayalakshmi as Rama
Balaji Venugopal as Dhayalan
K. S. Ravikumar as Police Commissioner
Aadukalam Naren as Film Producer
Radha Ravi as Producer
Chetan as Constable Mariadas
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A video about neural networks, how they work, and why they're useful.

My twitter: https://twitter.com/max_romana

SOURCES
Neural network playground: https://playground.tensorflow.org/

Universal Function Approximation:
Proof: https://cognitivemedium.com/ma....gic_paper/assets/Hor
Covering ReLUs: https://proceedings.neurips.cc..../paper/2017/hash/32c
Covering discontinuous functions: https://arxiv.org/pdf/2012.03016.pdf

Turing Completeness:
Networks of infinite size are turing complete: Neural Computability I & II (behind a paywall unfourtunately, but is cited in following paper)
RNNs are turing complete: https://binds.cs.umass.edu/pap....ers/1992_Siegelmann_
Transformers are turing complete: https://arxiv.org/abs/2103.05247

More on backpropagation:
https://www.youtube.com/watch?v=Ilg3gGewQ5U

More on the mandelbrot set:
https://www.youtube.com/watch?v=NGMRB4O922I

Additional Sources:
Neat explanation of universal function approximation proof: https://www.youtube.com/watch?v=Ijqkc7OLenI
Where I got the hard coded parameters: https://towardsdatascience.com..../can-neural-networks

Reviewers:
Andrew Carr https://twitter.com/andrew_n_carr
Connor Christopherson

TIMESTAMPS
(0:00) Intro
(0:27) Functions
(2:31) Neurons
(4:25) Activation Functions
(6:36) NNs can learn anything
(8:31) NNs can't learn anything
(9:35) ...but they can learn a lot

MUSIC
https://www.youtube.com/watch?v=SmkUY_B9fGg

This Edureka "ServiceNow Tutorial For Beginners" video will help you to get started with ServiceNow. This video mostly focuses on points like Cloud and Cloud Services, ServiceNow Applications and Processes.

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