Understanding Activation Functions: The 4 Most Commonly Used Types

In this video, we'll be exploring one of the most important concepts in neural networks: activation functions. Activation functions play a critical role in enabling neural networks to recognize patterns and make predictions. Without them, neural networks would not be able to learn and adapt to new data.

But what exactly is an activation function, and how does it work? In this video, we'll provide a clear and accessible explanation of this key concept, and introduce you to the four most commonly used types of activation functions: sigmoid, tanh, ReLU, and softmax.

We'll explain the strengths and weaknesses of each type, and provide real-world examples of when and where each type is most effective. We'll also discuss some of the key considerations to keep in mind when selecting an activation function for your own neural network projects.

Whether you're a beginner just getting started with neural networks or an experienced practitioner looking to deepen your understanding, this video has something for you. You'll come away with a clear understanding of what activation functions are, how they work, and which types are most commonly used in practice.

So be sure to check out our video on activation functions, and don't forget to hit the subscribe button to stay updated on all our future content!

TIMESTAMPS:
00:00 Background
01:19 Introduction
02:11 Neural Network Architecture
02:42 Input Layer
03:00 Hidden Layer
03:21 Output Layer
03:49 Feedforwards & Backpropagation
04:01 Feedforwards
04:28 Backpropagation
05:27 Why do neural networks need an activation function?
06:34 Types of Activation Functions
07:08 Threshold Function
08:33 Sigmoid Function
10:02 Rectifier Function
10:53 Hyperbolic Tangent (tanh)
12:09 Final thoughts

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