The growth in the amount of multimedia content available online supposes a challenge for search and recommender systems. This information in the form of visual elements is of great value to a variety of web mining tasks; however, the mining of these resources is a difficult task due to the complexity and variability of the images. In this paper, we propose applying a deep learning model to the problem of web categorization. In addition, we make use of a technique known as transfer or inductive learning to drastically reduce the computational cost of the training phase. Finally, we report experimental results on the effectiveness of the proposed method using different classification methods and features from various depths of the deep model.