Activation Functions In Neural Networks Explained | Deep Learning Tutorial

[@_Anna_Nass_]

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

[@reireireireireireireireirei]

Actuation functions.

[@kaiserkonok]

🤣

[@draziraphale]

These videos from Assembly AI are excellent. Distilled clarity

[@FarizDarari]

This video activates my understanding on activation functions!

[@terrylee6904]

Excellent Presentation.

[@deeplearningexplained]

Really sharp tutorial!

[@thepresistence5935]

Explained clearly

[@AssemblyAI]

thank you!

[@wagsman9999]

Thank you. I am a little smarter now!

[@igrok878]

thank you. Good pronouncing and good content.

[@oberstoffer]

wow ! really good explanation

[@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

[@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?

[@joguns8257]

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

[@AssemblyAI]

Glad you liked it

[@narendrapratapsinghparmar91]

Thanks for this informative video

[@bernardoolisan1010]

Very good video!

[@canygard]

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

[@ifeoluwarutholonijolu6944]

Can the softmax be used for a regression response

[@_dion_]

excellent.

[@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!

[@anurajms]

thank you

[@Rashad99990]

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

[@muskduh]

thanks

[@beypazariofficial]

nice

[@DahBot-nr7rf]

V can be W..

[@valentinleguizamon9957]

❤❤❤❤

[@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!