Deep Learning With Keras - Part I

The activation function sin() cannot be used as an activation function because it does not have the necessary properties required for an activation function. Activation functions are typically nonlinear and have a bounded range. The sin() function is periodic and does not have a bounded range, making it unsuitable for use as an activation function in neural networks.

As we progress through the layers of a convolutional neural network, the length and width of the activation of each layer usually decrease, while the depth or number of channels of each layer increases. This is because the convolutional layers apply filters to the input data, which reduces its spatial dimensions but increases its depth or number of channels. This allows the network to extract more complex and abstract features from the input data as we go deeper into the network. Therefore, the given answer is correct.

Activation functions like ReLU or sigmoid are used to introduce non-linearity in neural networks. Without activation functions, the neural network would only be able to learn and represent linear relationships between the input and output. However, in many real-world problems, the relationships are non-linear. Activation functions allow the neural network to learn and represent complex non-linear relationships, making it more powerful and capable of solving a wider range of problems.

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(63 – 7) / 2 + 1 = 29 => 29 x 29 x 32

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In maxpooling, "strides" refers to the number of pixels that the kernel should be moved by. This means that the kernel will move a certain number of pixels horizontally and vertically to cover the input image or feature map. By adjusting the stride value, we can control the amount of overlap between the kernel's receptive fields and the amount of downsampling that occurs in the output.

CNNs can be applied on any 2D and 3D array of data, not just limited to image and text data. This is because CNNs are designed to effectively capture spatial and temporal dependencies in data, making them suitable for various applications such as computer vision, natural language processing, and speech recognition. By using convolutional layers, pooling layers, and fully connected layers, CNNs can learn and extract meaningful features from different types of data, enabling them to perform tasks like image classification, object detection, and sentiment analysis.

When a max pool operation is applied with a stride of 2 and a filter size of 2 on a 16x32x32 input volume, the output volume will have a size of 16x16x16. This is because the filter moves across the input volume in steps of 2, taking the maximum value within each 2x2 region and creating a new output element. The resulting output volume will have a reduced spatial dimension of 16x16, while the depth remains the same at 16.

(300 x 300 x 3) x 100 + 100

Convolutional neural networks (CNNs) do not fully connect to all neurons in all the layers. Instead, they connect only to neurons in the local region (kernel size) of the input image. This local connectivity allows CNNs to efficiently extract features from images. Additionally, CNNs build feature maps hierarchically in every layer, meaning that they learn and extract increasingly complex features as they go deeper into the network. CNNs are inspired by the human visual system, which also processes visual information in a hierarchical and localized manner.

(5 x 5 x 3) x 100 + 100

The given statement is incorrect. In the KNN algorithm, increasing the value of K does not always result in an increase in accuracy. While a higher value of K may reduce the impact of noise in the data, it can also lead to over-smoothing and loss of important details. Additionally, increasing K can also increase the computational complexity and slow down the execution speed of the algorithm. Therefore, the statement that increasing K always improves accuracy but reduces the algorithm's execution speed is incorrect.

The statement "size of Input Image is reduced for 'VALID' padding" is true. Padding is a technique used in convolutional neural networks to preserve the spatial dimensions of the input image. In 'VALID' padding, no padding is added to the input image, resulting in a smaller output size compared to the input size. This is because the convolution operation only considers the valid positions of the kernel within the input image, without extending beyond the boundaries. Therefore, the output size is reduced when using 'VALID' padding.

اطلاعات بیشتر:
https://t.me/class_vision/159

An activation function is a mathematical function that determines the output of a neuron in a neural network. It takes the weighted sum of the inputs and applies a non-linear transformation to it, generating the output of the neuron. This output is then used as input for the next layer of the neural network. Therefore, the correct answer is "A function that triggers a neuron and generate the outputs".