Tutorial 2 -What Is Perceptron Single Layered Neural Network- Krish Naik Hindi

Perceptron is a single layer neural network and a multi-layer perceptron is called Neural Networks.
Perceptron is a linear classifier (binary). Also, it is used in supervised learning. It helps to classify the given input data.
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[@syedmustahsan4888]

Kamaal kar dia Karish bhai. Mai tang ho gia tha tutorials parh parh kai, but samaj ni aa raha tha. Aap nai bare ache sai samjha dia bhai.
Thank You Very much. Allah aap ko is ki JZA ata farmaey. Mere Rab ka be had shukr jis nai muje yeh channel suggest karwaya.

[@datadumpp]

i purchase PwSkills Course because of You but at a point where i start deep learning [Rishabh Malhotra] Don't know to teach
thankyou for proving this playlist as a resources for me
#please upload a video daily

[@user-kf4zh5lt2g]

Weights in a neural network don't directly activate or deactivate neurons. Instead, they modulate the strength or influence of input signals on the neuron's activation. Activation functions are responsible for how much should the neuron get activated........Chatgpt

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LOVE YOUR Teaching Sir

[@T1s_kashyap]

Nice ❤❤❤

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Pls Complete the stats playlist of this channel

[@hamzanadir7928]

I think we don't have any hidden layer in a single layered perceptron
it's the inputs connected to the output layer neurons

[@Nustian65]

sir please hurry up complete this tutorial paper he sir please good informative video

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Sir please complete end to end ml project playlist 🙂

[@MB-sh9ur]

far better explanation and quality

[@ZibaFact]

जय श्री राम ❤

[@SohanG3]

Summary :-
Perceptron / Artificial Neuron / Single Layered Neural Network :-
1. Input Layer - Analogy of eyes and shape and color of objects are features.
2. Hidden Layer - Single Layered NN has only one hidden layer.
We assign weights between input and hidden layers.
Hidden Layer Processing - 2 Steps.
Step 1
3. Weights are responsible for activating or deactivating neurons.
4. Bias - Weights are randonly assigned, so they can be zero and in multiplication whole thing can become zero thats why we add small bias.
Step 2:- Activation function
5. Transformation function
We keep updating weights to minimise the errors.
Perceptron Model - 1. Single Layer - Only used for linearly separable problem, not good for Non Linearly separable problem.
2. Multi Layer (ANN) - Has multiple hidden layers - Suitable for non linearly separable problems.

[@anshrai6]

teaching wrong