Training a Yes/No NN for Image Classes I am fairly a newbie to Neural Networks.
I wanted to ask if it is possible to train a NN to identify only one type object? For instance, a table from a large set of images, where the NN should be able to identify if new images are tables.
If yes can you please guide me in the direction to get started?

EDIT:
I wanted to ask if it is possible to train a NN to identify only one type object?...
so my understanding as of now is that if i train an NN on one class and one class alone with a fairly large amount of data then i can get it trained in a small amount of time rather than training it on a huge amount of data and consuming a larger amount of data...
Objective is to train a large no of such smaller NNs to create an ensemble of NNs.
Yes, you can train a NN to detect only one type of object like a table. However, you probably will not want to train such a NN from scratch by showing some examples of tables and non-tables. You will need to use transfer learning on a model already trained on several image classes and teach it to also recognize your new class. This transfer learning requires a smaller set of desired images. You may need to give it some negative examples also. You should explore transfer learning with mobilenet, inception, and other pre-trained Tensorflow models if you are willing to use Python and Tensorflow