Vaishali Kolhe

In deep learning, a model is typically considered "deep" if it has at least three layers. These layers include 1 input layer, 1 hidden layer, and 1 output layer. The hidden layer is crucial as it introduces non-linearity to the model, allowing it to learn complex patterns.

To differentiate deep learning from a basic ANN, a model typically requires at least these three layers. The presence of multiple hidden layers characterizes a deep learning model, in contrast to a simple ANN, which may have only one hidden layer. This depth enables deep learning models to learn and represent more complex patterns and features in the data.

Dear Vaishali Kolhe ,

How many layers do deep neural networks have? All neural networks have at least 2 layers: the input layer and the output layer. These 2-layer networks are the simplest we can make and train, but for that reason, they are also the most limited in their pattern learning and discrimination capabilities.

https://en.wikipedia.org/wiki/Deep_learning

Regards,

Shafagat

Dear Vaishali Kolhe ,

Deep learning and artificial neural networks (ANN) can be differentiated primarily by their structure and depth. ANNs consist of a few layers: an input layer, one or two hidden layers, and an output layer. This shallow architecture makes them suitable for simpler tasks where the data is not highly complex. In contrast, deep learning models are a subset of ANNs but are distinguished by their depth, having multiple hidden layers typically more than three. This deeper structure enables deep learning models to capture more complex patterns and relationships within the data. As a result, deep learning models, which include Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), can handle more intricate and large-scale data problems. Therefore, the key difference between the two lies in the number of layers, with deep learning models having a deeper architecture that allows for more sophisticated feature learning and representation.

There is no difference between the terms Deep Learning and Artificial Neural Networks, just the former is more modern while the latter is more traditional. If at all, ANNs might be the more generic term, since it encompasses DL, but I typically use these terms as synonyms.