Deep Learning Codes And Docs
Deep Learning is a branch of Machine Learning that focuses on creating Artificial Neural Networks that can mimic the human brain's ability to learn and make decisions. The term "deep" refers to the multiple layers that make up these neural networks.
Deep Learning has been widely used in various applications, such as image recognition, natural language processing, and speech recognition. Convolutional Neural Networks (CNNs) are a type of neural network that is particularly useful for image and video recognition.
ResNet is a type of CNN architecture that was introduced in 2015 by researchers from Microsoft Research. ResNet stands for Residual Network, and it is a deep neural network that uses residual connections to overcome the vanishing gradient problem.
Convolutional Neural Networks (CNNs) are a type of neural network that is particularly useful for image and video recognition. CNNs use a technique called convolution, which involves applying a set of filters to the input image to extract features.
The filters are learned during the training process, and they are used to identify patterns and features in the image. CNNs also use pooling layers to reduce the size of the feature maps and increase the network's robustness to variations in the input.
Residual Networks (ResNets) are a type of CNN architecture that was introduced in 2015 by researchers from Microsoft Research. ResNets use residual connections to overcome the vanishing gradient problem, which can occur in deep neural networks.
The vanishing gradient problem occurs when the gradients become very small as they propagate through the layers of the network. This can make it difficult to train deep neural networks because the gradients become too small to make significant updates to the network's parameters.
ResNets use residual connections to skip over one or more layers in the network, allowing the gradients to flow more easily through the network. The residual connections also make it easier to train deeper networks, which can lead to better performance on complex tasks.
Conclusion Deep Learning is a powerful approach to building Artificial Neural Networks that can learn and make decisions like humans. CNNs are a type of neural network that is particularly useful for image and video recognition, and ResNets are a type of CNN architecture that uses residual connections to overcome the vanishing gradient problem and make it easier to train deeper networks.