Basics of Deep Learning Part 15: Coding a Neural Network from Scratch in Python

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In this series we are going to cover the basics of deep learning. And in this video we are going to code a neural network from scratch using NumPy and Pandas.

Links:
- Videos that I mentioned at 25:18 :

Sebastian Mantey

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The actual coding of the neural net starts at 12:26

SebastianMantey

Thank you for creating a great content!

fakhriddinmakhmadiyorov

i have watched whole series on Basics of Deep Learning Part 1-15: Coding a Neural Network from Scratch in hooked to it and completed on binge.

daljeetsinghranawat

Hello sebastian, i have watched all your videos and learned quite a lot .
you haven't posted any video in quiet sometime, please don't quit making such great and informative from scratch videos
also please make CNN, RNN and others from scratch videos

yashmaheshwari

Thank you Sebastian Mantey, this course of your has been very helpful. I will recommend it to others. I just have one question
How would you test the accuracy if you have three classes . in other words three output nodes?

orlin

This is Epic ...A legendary tale of Neural Nets...

MrAnandml

Sir can you create video on Batch normalization in neural network from scratch. Its the most widely used technique in deep learning

nehathakur

Hi,
Thanks for detailed explanation. I have a question.
At 10:32, what does n_hidden mean? Is that number of hidden layers? If there are four hidden layers in this network then we should also have more than two steps of weights.
We have one input layer, 4 hidden layer and one output layer. so, I was expecting W1 as weight b/w input and 1st hidden, W2 b/w H1 and H2, and so on
Please let me know if I misunderstood this concept

Thanks in advance
Regards

hassansherwani

Great vid!
Thanks for starting again from scratch. In the previous video couple of things didn't work. At least in my Python

jcoixgz

A great content but why you quitted YouTube
Some people wants these kinds of informative videos from scratch.

surajjoshi

Hi, thank you for your help
could you tell me how to solve this problem while coding on jupyter notebook
RuntimeWarning: overflow encountered in exp
return 1/(1+np.exp(-x))

ValueError Traceback (most recent call last)
in <module>
4 output_layer=sigmoid(np.dot(hiden_layer, weight_2))
5 #back propagation
----> 6
7 hiden_delta=np.dot(output_delta, weight_2.T)*hiden_layer*(1-hiden_layer)
8

in f(self, other, axis, level, fill_value)
645 # TODO: why are we passing flex=True instead of flex=not special?
646 # 15 tests fail if we pass flex=not special instead
--> 647 self, other = _align_method_FRAME(self, other, axis, flex=True, level=level)
648
649 if isinstance(other, ABCDataFrame):

in _align_method_FRAME(left, right, axis, flex, level)
488
489 else:
--> 490 raise ValueError(
491 "Unable to coerce to DataFrame, shape "
492 f"must be {left.shape}: given {right.shape}"

ValueError: Unable to coerce to DataFrame, shape must be (100, 15): given (396, 9)

yonasbelay

hi i tried as per your steps, i am having the following error :

ImportError Traceback (most recent call last)
in <module>
4 from sklearn.model_selection import train_test_split
5 from sklearn import preprocessing
----> 6 from sklearn import MinMaxScaler
7
8

ImportError: cannot import name 'MinMaxScaler' from 'sklearn'

nirani

Hi,
I really appreciate the amount of effort you put for this content. Could you please make mathematical plus from scratch for other Deep learning topics such as RNN, CNN, word embedding etc? Just a suggestion !

Regards

hassansherwani

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