Deep Neural Network Python from scratch | L layer Model | No Tensorflow

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural Network with L layers in python from scratch.
This video is for those enthusiasts who love to know under-the-hood details behind how things work. You can directly use the TensorFlow model to create a Deep Neural Network, but if you are curious to know how things work in python from scratch, then this video is for you.
Understanding Deep Neural Network in Python from scratch helps you learn how deep learning actually works and gives you confidence in understanding Machine Learning.
And if you have followed my playlist on Neural Network, then writing this code will be super simple for you. I have tried to explain a very difficult code in a simple manner, so please let me know in the comments section what you feel about this video.
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Timestamps:
0:00 Coming Next
0:30 Intro
3:14 Overview
6:32 Initializing Parameters
14:57 Forward Propagation
23:36 Cost Function
26:22 Backward Propagation
33:10 Update Parameters
34:23 Complete Model
40:36 Improving Model Look
48:44 End
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📕 Complete Code and Assignment: github.com/Coding-Lane/Deep-N...
📕 Neural Network Playlist: • How Neural Networks wo...
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✔ CNN Playlist: • What is CNN in deep le...
✔ RNN Playlist: • How Neural Networks wo...
✔ Logistic Regression Playlist: • Logistic Regression Ma...
✔ Linear Regression Playlist: • What is Linear Regress...
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If you want to ride on the Lane of Machine Learning, then Subscribe ▶ to my channel here: / @codinglane

[@hamafel7559]

Dude....your videos are FABULOUS!!
keep going!!we need you!!

[@Felloggs]

Bro, your content and you as well are AWESOME.
Liked and subbed, keep it up!

[@lecturesfromleeds614]

Straight forward and to the point! Good video

[@ikrameounadi7075]

hey , great playlists , i can now say i can really understand deep learning , can you please make a video explaining the perceptron algorithm and its complexity along with kernels ?!

[@lokendrakumar6212]

Bro this is the simplest explanation in whatever I have seen

[@soumyadeepsarkar2119]

thanks a lot bro your videos really helped me

[@threetime-ne2dc]

thank u, it the best and clear vedio I have watched😍U an extremely handsome man!!!

[@subhambasuroychowdhury9698]

Awesome Video man!!!

[@matthiasblumrich6802]

You and Josh Starmer (StatQuest) totally demystify DNNs. Thanks!!!

[@CodingLane]

Hey.. thats a big compliment. Thanks!

[@user-or2gv8zf4l]

Hi, Mr. Jay Patel!
Thanks a lot for such explaining!
Why you don't use derivative in the output layer (AL) for sigmoid function during backward pass?
Can we state that the weights of the last layer (WL) learn without taking into account back pass of the output error (AL-Y) through sigmoid?
If yes, why you and other guys don't use it?

[@divyagarh]

Good stuff. No videos for a year? please keep uploading. Thank you

[@jatingupta3443]

Thanks bro

[@svk0071]

Very helpful. Please make a playlist for GAN and transformers like you made for CNN.

[@confuseartist831]

you're back....damnnnnn💥💥

[@CodingLane]

Haha… Thanks! 😁

[@ajay0909]

very decent explanation, would you like to do the same for CNN?

[@mustafatuncer4780]

I really liked it. I would also like for you to create a video about LSTMs and Transformers (from scratch).

[@CodingLane]

Thanks for the suggestion :)

[@shubhamraj310]

keep it up✌️💯

[@CodingLane]

Thanks Shubham 😄

[@prajwalsatannavar4576]

Too Good man....Thank u so much!

[@CodingLane]

You’re welcome!

[@prajwalsatannavar4576]

Train_x data is not working...
@@CodingLane

[@zonunmawiazadeng5018]

Hey I implemented Backpropagation on CNN.....dL/dZ = dL/dA dot product on dA/dZ according to your video....In my implemented, dL/dA and dA/dZ are both the shape (training size,image height, image weight, channel size)...If this is corrected, how should we dot product it....

[@rodrigoyepez8123]

I keep getting this error ( ValueError: shapes (1,1) and (10,1000) not aligned: 1 (dim 1) != 10 (dim 0) ) in reference to this line ( grads["dZ" + str(l)] = np.dot(parameters['W' + str(l+1)].T,grads["dZ" + str(l+1)])*derivative_relu(forward_parameters['A' + str(l)]) ) does anyone know why?

[@yulupi552]

great content, would you like to do the same for RNN?

[@ashraf_isb]

Thank you so much sir, very much helpful 🙂

[@CodingLane]

Welcome!

[@user-gf2hp2ij4x]

​@@CodingLane please make a playlist on YOLO algorithm

[@ongyinren3secv96]

I had a problem with the output in Spyder that it only show one iteration which is
iter:1 cost: 0.697567606727616 train_acc:0.65 test_acc:0.3
==
May I know what possibly is the error?

[@uchindamiphiri1381]

is this video suitable for beginers? If not recommend me what to watch before jumping to this

[@tungvuson4682]

hi. what is your dataset name

[@niladrichakraborti5443]

Please make a video on COVID-19 detection using chest X Ray

[@chandut1296]

Hi bro, try to do the videos on Pre-trained hugging face transformers

[@CodingLane]

Thanks for your suggestion… will try to make a video on it

[@algorithmo134]

You made a small mistake while typing the code for derivative_tanh(x) function.
✔ The correct code will be :
def derivative_tanh(x):
return 1 - np.power(np.tanh(x), 2)

[@pranavgangapurkar195]

you are SVNIT passout na?

[@CodingLane]

Hi Pranav. Yea, I am from SVNIT

[@TheCodingChamelion]

copied from coursera

[@CodingLane]

Haha.. yeah, I have learned from that only. It's a very good source tbh.
I have made a video on CZcams, bcoz they don't have any video of it.