This paper presents the Adam Mickiewicz University's (AMU) submissions to the WMT 2021 News Translation Task. The submissions focus on the English↔Hausa translation directions, which is a low-resource translation scenario between distant languages. O
ur approach involves thorough data cleaning, transfer learning using a high-resource language pair, iterative training, and utilization of monolingual data via back-translation. We experiment with NMT and PB-SMT approaches alike, using the base Transformer architecture for all of the NMT models while utilizing PB-SMT systems as comparable baseline solutions.
The project aims primarily to employ the benefits of artificial intelligence, specifically the characteristics of programming a neuronal network where neuronal networks, in turn, are networks that are interested in trainin
g and learning from error, and employing this error to achieve optimal results.Convolution NeuralNetworks(CNN)in particular are one of the most important neuronal networks that address classification problems and issues. Thus, this project is to design a convolution neuronal network that classifies vehicles into several types where we will design the network and train them on the database as the database includes pictures of several types of vehicles The network will classify each Image to its type, after adjusting the images, making the appropriate changes, turning them gray, and discovering the edges and lines.After the images are ready, the training process will begin, and after the training process is finished, we will produce classification results, and then we will test with a new set of images.One of the most important applications of this project is to abide by the paving places of cars, trucks, and vehicles in general, as if a picture was entered as a car for the car sample, which is a truck, for example, this will give an error where the network will discover this by examining and classifying it. As a truck, we discover that there is a violation of the paving laws
