Activation Functions In Neural Networks Explained | Deep Learning Tutorial

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In this video we are going to learn about Activation Functions in Neural Networks. We go over:
* The definition of activation functions
* Why they are used
* Different activation functions
* How to use them in code (TensorFlow and PyTorch)
Deep Learning In 5 Minutes video: • Deep learning in 5 min...
Different activation functions we go over:
Step Functions, Sigmoid, TanH, ReLU, Leaky ReLU, Softmax
Timestamps:
00:00 Introduction
00:35 Activation Functions Explained
01:48 Different activation functions
05:23 How to implement them
06:20 Get your Free AssemblyAI API link now!

[@reireireireireireireireirei]

Actuation functions.

[@kaiserkonok]

🤣

[@_Anna_Nass_]

OMG, you actually made this easy to understand. I can't believe it. The animations are so helpful. Thank you immensely!

[@draziraphale]

These videos from Assembly AI are excellent. Distilled clarity

[@oberstoffer]

wow ! really good explanation

[@machinelearningexplained]

Really sharp tutorial!

[@alpeshdongre8196]

🎯 Key Takeaways for quick navigation: 01:35 🧠 *Activation functions are crucial in neural networks as they introduce non-linearity, enabling the model to learn complex patterns. Without them, the network becomes a stacked linear regression model.* 02:43 🔄 *The sigmoid function, commonly used in the last layer for binary classification, outputs probabilities between 0 and 1. It's effective for transforming very negative or positive inputs.* 03:25 ⚖️ *Hyperbolic tangent, ranging from -1 to +1, is often chosen for hidden layers. ReLU (Rectified Linear Unit) is simple but effective, outputting the input for positive values and 0 for negatives, addressing the dying ReLU problem.* 04:32 🔍 *Leaky ReLU is a modification of ReLU that prevents neurons from becoming "dead" during training by allowing a small output for negative inputs. Useful in hidden layers to avoid the dying ReLU problem.* 05:13 🌐 *Softmax function is employed in the last layer for multi-class classification, converting raw inputs into probabilities. It's commonly used to determine the class with the highest probability.* Made with HARPA AI

[@terrylee6904]

Excellent Presentation.

[@wagsman9999]

Thank you. I am a little smarter now!

[@igrok878]

thank you. Good pronouncing and good content.

[@narendrapratapsinghparmar91]

Thanks for this informative video

[@bernardoolisan1010]

Very good video!

[@_dion_]

excellent.

[@thepresistence5935]

Explained clearly

[@AssemblyAI]

thank you!

[@anurajms]

thank you

[@ianlinify]

Excellent explanation! Very easy to understand this complex concept through your clear presentation. By the way, it looks like in some cases we don't need to include an activation function in layers, any explanation about why sometimes activation functions are not necessary?

[@muskduh]

thanks

[@joguns8257]

Superb introduction. Other videos have just been vague and hazy inn approach.

[@AssemblyAI]

Glad you liked it

[@bezelyesevenordek]

nice

[@rashadloulou]

We could apply an AI tool to this video to replace actuation with activation :D

[@be_present_now]

Good video! One thing I want to point out is that the presenter is talking too fast, a slower speed would make the video great!

[@valentinleguizamon9957]

❤❤❤❤

[@canygard]

Why was the ReLU neuron so depressed? ...It kept getting negative feedback, and couldn't find any positive input in its life.

[@B_knows_A_R_D-xh5lo]

😊😊😊😊🎉🎉🎉🎉

[@DahBot-nr7rf]

V can be W..

[@sumanbhattacharjee7550]

real life Sheldon Cooper

[@brianp9054]

it was said but worth the emphasis, ... 'actuation' function 🤣🤣🤣. Repeat after me, one two and three: A-C-T-I-V-A-T-I-0-N. Great, now keep doing it yourself until you stop saying actuation function...

[@Huffman_Tree]

Ok I'll give it a try: Activatizeron!