Activation Functions in Artificial Neural Networks

Study Notes

Three 28*28 pixel images are presented, but your mind has no trouble recognizing them as three, despite the distinct pixel values of sensitive cells in your eyes that respond differently to each image.
This intelligent part of the brain, known as the visual cortex, can present the concept of the number three, yet simultaneously identify other images as different concepts, even if the pixel values vary greatly.
The visual cortex receives a 28*28 pixel input image, with each pixel having a specific value that represents the brightness level. This value within the pixel is called an activation.
The input image is made up of 784 pixels in total, each pixel being an activation unit that the network believes represents a certain number, based on the input image.
The network also consists of hidden layers, with the second layer containing ten activation units, each one representing a specific activation value from the input image, and the network interprets the input image as that particular number.
The input image is also processed through concealed layers, which currently serve only as a significant confusion if there is significant confusion about how the identification process works.
The network functions by each pixel having an activation value, which corresponds to a certain number, allowing the computer to recognize patterns and understand the input image.- The text discusses the functioning of artificial neural networks, specifically focusing on the activation functions used in these networks.
Two hidden layers, each with 16 neurons, were chosen for this network. The choice was made based on the desire to activate the network in one instance and the convenience of 16 as a good fit for the screen during training.
There are many neurons competing for activation, so which one is activated depends on the weighted sum of inputs from the previous layer.
The activation function for the neurons in the hidden layers is sigmoid, while the activation function for the output layer is not specified in the text.
Sigmoid function converts inputs to outputs between 0 and 1, with an S-shaped curve. It is sensitive to small inputs and saturates for large inputs.
The goal is to find the appropriate weights and biases for the network through training, which involves adjusting these values based on error between the predicted and actual outputs.
The backpropagation algorithm is used to calculate the error gradient and adjust the weights and biases accordingly.
The sigmoid function is differentiable, allowing for a smooth calculation of the gradient. This is important for the backpropagation algorithm to work effectively.
The neural network is trained by adjusting the weights and biases based on the error gradient, with the goal of minimizing the error between the predicted and actual outputs.
The sigmoid function can be replaced with other activation functions, such as Rectified Linear Unit (ReLU), which is simpler and more commonly used in modern neural networks.
ReLU function only activates the neuron if the input is greater than a certain threshold, making it less computationally expensive and more efficient than sigmoid.
The author mentions a person named Lisha who holds a doctorate in computer science and works for a company called Amplify Partners, which provided some funding for this video project.
The text discusses the history of neural networks and activation functions, with sigmoid being an earlier choice for activation functions, but Relu being more commonly used today.
The text also mentions the importance of the activation function in the neural network and how it affects the network's performance.
The text concludes by encouraging viewers to subscribe to the channel for more videos and thanking them for their support.

Description

Explore the functioning of artificial neural networks, focusing on the activation functions used in these networks. Learn about the role of activation values, hidden layers, and the training process involving backpropagation algorithm. Understand the significance of activation functions like sigmoid and ReLU, and their impact on network performance.