difference in between CNN and Inception v3
The Inception models are types on Convolutional Neural Networks designed by google mainly for image classification. Each new version (v1, v2, v3, etc.) marks improvements they make upon the previous architecture.

The main difference between the Inception models and regular CNNs are the inception blocks. These involve convolving the same input tensor with multiple filters and concatenating their results. Such a block is depicted in the image below.



On the contrast, regular CNNs performs a single convolution operation on each tensor. 

Inception-v3 is Deep Neural Network architecture that uses inception blocks like the one I described above. It's architecture is illustrated in the figure below.



The parts where the layers "branch off" and then are merged together again are the inception blocks described previously.

You can read more about Inception-v3 here.