This paper gives a perspective about how Google Maps, one of the world's most influential application works. Google Maps was initially coded in C++ programming language by its founders-Lars and Jens Eilstrup Rasmussen. Formerly it was named 'Where 2 Technologies', which was later acquired by Google Inc. in 2004, which renamed this web-application to Google Maps. Earlier it had limited features restricted to navigation, but today it provides overwhelming features like street-view, ETA and other such intriguing features. It gives an overview about the algorithms and procedures employed by Google Maps to carry out analysis and enable users to carry out desired operations. Various features provided by Google Maps are portrayed in this paper. It describes the algorithms and procedures used by Google Maps to find the shortest path, locate one's position, geocoding and other such elegant features it provides its users.
Different mathematical models, Artificial Intelligence approach and Past recorded data set is combined to formulate Machine Learning. Machine Learning uses different learning algorithms for different types of data and has been classified into three types. The advantage of this learning is that it uses Artificial Neural Network and based on the error rates, it adjusts the weights to improve itself in further epochs. But, Machine Learning works well only when the features are defined accurately. Deciding which feature to select needs good domain knowledge which makes Machine Learning developer dependable. The lack of domain knowledge affects the performance. This dependency inspired the invention of Deep Learning. Deep Learning can detect features through self-training models and is able to give better results compared to using Artificial Intelligence or Machine Learning. It uses different functions like ReLU, Gradient Descend and Optimizers, which makes it the best thing available so far. To efficiently apply such optimizers, one should have the knowledge of mathematical computations and convolutions running behind the layers. It also uses different pooling layers to get the features. But these Modern Approaches need high level of computation which requires CPU and GPUs. In case, if, such high computational power, if hardware is not available then one can use Google Colaboratory framework. The Deep Learning Approach is proven to improve the skin cancer detection as demonstrated in this paper. The paper also aims to provide the circumstantial knowledge to the reader of various practices mentioned above.
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