The stochastic gradient descendant algorithm is one of the most popular neural network training algorithms. Many authors have contributed to modifying or adapting its shape and parametrizations in order to improve its performance. In this paper, the authors propose two modifications on this algorithm that can result in a better performance without increasing significantly the computational and time resources needed. The first one is a dynamic learning ratio depending on the network layer where it is applied, and the second one is a dynamic drop-out that decreases through the epochs of training. These techniques have been tested against different benchmark function to see their effect on the learning process. The obtained results show that the application of these techniques improves the performance of the learning of the neural network, especially when they are used together.