This thesis explores the relatively new field of constructing and displaying artificial neural networks using web technologies and compares the performance of neural networks trained with web technologies and with the currently most popular machine learning framework. In the first part of this thesis neural networks are explained, what they are, how they evolved, what some of the more popular learning types are and what their differences are and how a shallow learning and a deep learning neural network operate, both of which can be created, trained and used in the practical part of the thesis. The learning algorithm is particularly explored, which is the one that the both supported neural network architectures use, with slight difference, all to demonstrate the complexities included in the training process which is later used to compare the performances of two different implementations of neural network applications. The next part of the thesis shows the web application that was created using purely web technologies as a proof that the technologies have evolved sufficiently to allow creating such applications. Because deep learning became very popular in the field of artificial intelligence and is widely used in the world, as an example that the web application can support that kind of neural networks, the LSTM (Long Short-term Memory) architecture is selected. The LSTM network is popular in the field of natural language processing. After that the performance of the web application is compared with TensorFlow, the currently most popular neural network framework which performs the training by compiling the code and then running it. In the last part of this thesis explores the second part of the project which uses TensorFlow for faster and more efficient training and usage of neural networks. The application is based on current web development standards and is created to be easily extendable.