Thanks for reaching out to us, they are all actually neutral networking, a brief overview of each type and their differences as below, please take a look at it and let me know if you have any questions.
ANN (Artificial Neural Network): ANN is a type of neural network that consists of multiple layers of interconnected nodes. Each node in the network receives input from the nodes in the previous layer and produces output that is passed to the nodes in the next layer. ANN is a general-purpose neural network that can be used for a wide range of tasks, including classification, regression, and pattern recognition.
CNN (Convolutional Neural Network): CNN is a type of neural network that is commonly used for image recognition and computer vision tasks. CNNs use a process called convolution to extract features from images. The convolution process involves sliding a small window (called a filter or kernel) over the image and performing a mathematical operation on the pixels within the window. The output of the convolution operation is a feature map that highlights the important features of the image.
RNN (Recurrent Neural Network): RNN is a type of neural network that is commonly used for natural language processing and time series analysis. RNNs use a process called recurrence to process sequences of data. The recurrence process involves passing the output of each node in the network back to the input of the same node in the next time step. This allows the network to maintain a memory of previous inputs and produce output that depends on the entire sequence of inputs.
To implement these models with the best scenario, you need to consider the specific requirements of your task.
For example, if you are working on an image recognition task, a CNN may be the best choice. If you are working on a natural language processing task, an RNN may be the best choice. If you are working on a general-purpose task, an ANN may be the best choice.
It's also important to consider the size and complexity of your dataset, as well as the computational resources available to you. Some models may be more computationally intensive than others and may require more powerful hardware to train and run.
I hope this helps! Let me know if you have any further questions.
Regards,
Yutong