Mastering Convolutional Neural Networks (CNNs) for Deep Learning Applications
What is Deep Learning?
Deep Learning is a subset of machine learning that involves the use of artificial neural networks with multiple layers to model and solve complex problems. These networks are designed to learn from vast amounts of data and can identify patterns and relationships in the data, making it possible to make accurate predictions or classifications.
Deep Learning algorithms have been successful in a wide range of applications such as image and speech recognition, natural language processing, and self-driving cars.
What is Convolutional Neural Network?
CNN, short for Convolutional Neural Network, is a type of machine learning algorithm that is commonly used in image and video recognition tasks. CNN is a type of neural network that applies convolutional layers, pooling layers, and fully connected layers to the input data.
The CNN algorithm has revolutionized the field of computer vision by achieving state-of-the-art results on many image and video recognition tasks. In contrast to traditional machine learning algorithms, which require manually engineered features to be extracted from the input data, CNNs can learn these features automatically from the data itself.
The CNN architecture consists of several layers, each of which performs a specific function. The first layer is a convolutional layer, which applies a set of filters to the input data. The filters scan the input data and identify certain patterns and features, such as edges and corners, that are important for the classification task.
The output of the convolutional layer is then passed through a pooling layer, which reduces the dimensionality of the data by downsampling it. This helps to reduce the computational complexity of the algorithm and prevents overfitting.
The next layer is a fully connected layer, which takes the output of the previous layer and applies a set of weights to it. This layer learns to map the features identified in the previous layers to the desired output, such as a classification label.
The output of the fully connected layer is then passed through a softmax activation function, which converts the output to a probability distribution over the possible classes. The class with the highest probability is then chosen as the predicted class.
CNNs are trained using a variant of the backpropagation algorithm, which adjusts the weights in the network to minimize the difference between the predicted output and the true output. The weights are adjusted using gradient descent, which involves iteratively computing the gradient of the loss function with respect to the weights and updating the weights in the direction of the negative gradient.
Depth Explanation
→ Convolution Layer
Convolution is the first layer to extract features from an input image. Convolution preserves the relationship between pixels by learning image features using small squares of input data. It is a mathematical operation that takes two inputs such as an image matrix and a filter or kernel.
Consider a 5 x 5 whose image pixel values are 0, 1 and filter matrix 3 x 3 as shown below
Then the convolution of 5 x 5 image matrix multiplies with 3 x 3 filter matrix which is called “Feature Map” as output shown in below
Convolution of an image with different filters can perform operations such as edge detection, blur, and sharpen by applying filters. The below example shows various convolution images after applying different types of filters (Kernels).
→ Strides
Stride is the number of pixels shifts over the input matrix. When the stride is 1 then we move the filters to 1 pixel at a time. When the stride is 2 then we move the filters to 2 pixels at a time and so on. The below figure shows convolution would work with a stride of 2.
→ Padding
Sometimes filter does not fit perfectly fit the input image. We have two options:
- Pad the picture with zeros (zero-padding) so that it fits
- Drop the part of the image where the filter did not fit. This is called valid padding which keeps only a valid part of the image.
→ Non-Linearity (ReLU)
ReLU stands for Rectified Linear Unit for a non-linear operation. The output is ƒ(x) = max(0,x).
Why ReLU is important: ReLU’s purpose is to introduce non-linearity in our ConvNet. Since the real-world data would want our ConvNet to learn would be non-negative linear values.
There are other non linear functions such as tanh or sigmoid that can also be used instead of ReLU. Most of the data scientists use ReLU since performance wise ReLU is better than the other two.
→ Pooling Layer
Pooling layers section would reduce the number of parameters when the images are too large. Spatial pooling also called subsampling or downsampling which reduces the dimensionality of each map but retains important information. Spatial pooling can be of different types:
- Max Pooling
- Average Pooling
- Sum Pooling
Max pooling takes the largest element from the rectified feature map. Taking the largest element could also take the average pooling. Sum of all elements in the feature map call as sum pooling.
→ Fully Connected Layer
The fully connected (FC) layer in a neural network is a type of layer where every neuron in the layer is connected to every neuron in the preceding layer. In the context of CNNs, the input data is flattened into a vector and fed into the FC layer, which then performs a linear transformation on the input vector followed by an activation function. The FC layer allows the network to learn complex, non-linear relationships between the input data and the output, making it an important component of many CNN architectures.
In the above diagram, the feature map matrix will be converted into a vector. With the fully connected layers, we combined these features together to create a model.
Convolutional Neural Network (CNN) Architecture for Image Classification:
Summary Step by Step
- Provide input image into convolution layer
- Choose parameters, apply filters with strides, and padding if requires. Perform convolution on the image and apply ReLU activation to the matrix.
- Perform pooling to reduce dimensionality size
- Add as many convolutional layers until satisfied
- Flatten the output and feed into a fully connected layer (FC Layer)
- Output the class using an activation function (Logistic Regression with cost functions) and classifies images.
Top Machine Learning Mastery: Elevate Your Skills with this Step-by-Step Tutorial
1. Need for Machine Learning, Basic Principles, Applications, Challenges
4. Logistic Regression (Binary Classification)
8. Gradient Boosting (XGboost)
11. Neural Network Representation (Perceptron Learning)
15. Dimensionality Reduction (PCA, SVD)
16. Clustering (K-Means Clustering, Hierarchical Clustering)
19. Reinforcement Learning Fundamentals and Applications
20. Q-Learning
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