"Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization."
A type of deep neural network commonly used in image recognition tasks. They use filters to scan and identify features within images.
Image Processing: Understanding the basics of image processing is essential for developing Convolutional Neural Networks (CNN). Image processing involves techniques such as filtering, transformation, segmentation, and object detection.
Convolution: Convolution is a mathematical operation that is at the heart of CNNs. It involves applying a filter (or kernel) to an input image to produce an output feature map.
Pooling: Pooling is a technique used in CNNs to reduce the spatial size of the input image while retaining important features. Common types of pooling include max pooling and average pooling.
Activation functions: Activation functions are used in neural networks to introduce non-linearity into the model. Common activation functions used in CNNs include ReLU, sigmoid, and tanh.
Backpropagation: Backpropagation is a popular training algorithm used in neural networks. In CNNs, it is used to update the values of the network's weights and biases in order to minimize the error or loss function.
Transfer Learning: Transfer learning is a technique that involves using pre-trained CNN models for a specific task and reusing it for another related task. This helps to reduce the amount of data and training required, resulting in a faster and more effective model.
Object Detection: Object detection involves detecting and localizing objects within an image or video. Popular object detection algorithms used in CNNs include YOLO, RCNN, and Faster-RCNN.
Image Segmentation: Image segmentation is the process of dividing an image into multiple segments or regions based on some criteria. This is a critical task in computer vision and is used in a variety of applications, including medical imaging, autonomous driving, and robotics.
Data Augmentation: Data augmentation is a technique used to artificially increase the size of the training data set by applying different transformations to the images such as rotation, flipping, scaling, and random cropping.
Transfer Learning: Transfer learning employs pre-trained CNN models on a specific task and reuse them for another related task. This method reduces the amount of data and training required.
LeNet: One of the first CNNs developed for handwritten digit recognition. It contains two convolutional layers, two subsampling layers, and three fully connected layers.
AlexNet: A deep CNN that won ImageNet ILSVRC-2012 competition. It has five convolutional layers, three fully connected layers, and employs dropout regularization.
VGG: Consists of multiple layers with small 3×3 convolutional filters. The most common version has 16 layers, and its output is passed to three fully connected layers.
GoogLeNet (Inception Networks): It consists of multiple layers of convolutions and also employ Inception modules that combine multiple different convolutional operations.
ResNet: A CNN with residual learning. It has a unique architecture that enables the training of very deep networks. Instead of learning residual functions, it learns the residual mapping between input and output.
DenseNet: A CNN that uses dense connections. It ensures that each layer receives the feature maps from all preceding layers. It also promotes feature reuse.
MobileNet: A lightweight CNN that is optimized for mobile devices. It employs depth-wise separable convolutions to reduce the computational requirements.
YOLO (You Only Look Once): A real-time object detection system that divides an image into a grid of cells and predicts bounding boxes and class probabilities in each cell.
Mask R-CNN: Combines object detection and instance segmentation. It is an extension of Faster R-CNN, and it predicts object instances as well as their exact masks for better object delineation.
Siamese Networks: A CNN that is trained to compare two input images and predict whether they are similar or different. It is commonly used for object tracking and face recognition.
FPN (Feature Pyramid Networks): Used for multi-scale object detection. It generates feature maps at different scales and combines them to detect objects of varying sizes.
U-Net: A CNN architecture used for image segmentation. It has a contracting path that encodes an input image and an expanding path that decodes it to obtain a segmentation mask.
DCGAN (Deep Convolutional Generative Adversarial Networks): A CNN architecture used for image generation. It employs a generator and discriminator network that are trained in tandem to generate realistic images.
GPT (Generative Pre-trained Transformer): A CNN that is commonly used for natural language processing tasks such as language modeling and text generation.
EfficientNet: A CNN that is optimized for both accuracy and computational efficiency. Its architecture uses a combination of mobile inverted bottleneck blocks and efficient attention mechanisms to achieve state-of-the-art results with fewer parameters.
"Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections."
"For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels."
"However, applying cascaded convolution (or cross-correlation) kernels, only 25 neurons are required to process 5x5-sized tiles."
"They have applications in image and video recognition, recommender systems, image classification, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series."
"CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), based on the shared-weight architecture of the convolution kernels or filters."
"Counter-intuitively, most convolutional neural networks are not invariant to translation, due to the downsampling operation they apply to the input."
"Typical ways of regularization, or preventing overfitting, include penalizing parameters during training (such as weight decay) or trimming connectivity (skipped connections, dropout, etc.)"
"Robust datasets also increase the probability that CNNs will learn the generalized principles that characterize a given dataset rather than the biases of a poorly-populated set."
"Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex."
"CNNs use relatively little pre-processing compared to other image classification algorithms."
"This means that the network learns to optimize the filters (or kernels) through automated learning."
"In traditional algorithms, these filters are hand-engineered."
"This independence from prior knowledge and human intervention in feature extraction is a major advantage."
"Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization."
"Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections."
"For each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels."
"However, applying cascaded convolution (or cross-correlation) kernels, only 25 neurons are required to process 5x5-sized tiles."
"Recommender systems, image segmentation, medical image analysis, natural language processing, brain–computer interfaces, and financial time series."
"This independence from prior knowledge and human intervention in feature extraction is a major advantage."