Text classification is a subset of machine learning which is used to classify texts such as tweets, email, news headlines or articles, with tags or categories. As news publishing can have uncertainty in their categorisations, text classification could categorise articles autonomously and distinguish unclear categorisations. The library TensorFlow helps with operations and tools for the machine learning workflow.  This paper takes focus on the crucial parts of working with machine learning using TensorFlow.js and to what extent this model can categorise a news article. The authors evaluates different models to analyse how optimising the settings will affect the accuracy of the model. Results of this paper was researched with a literature study of official documentations and peer reviewed reports. An empirical experiment where machine learning models were trained in TensorFlow.js was also performed. The results showed that the model with the highest accuracy with 87.17% accuracy was trained with 1000 articles using Relu and Softmax activation functions and the Mean squared error loss function. While the model with lowest accuracy had 75.5% using Sigmoid activation functions and Categorical cross-entropy on the 5000 articles training set. Crucial parts for this development were: optimizer function, loss function, batch size, activation functions, training data and test data with labels, normalise function, shapes of layers and computing power. There are several parts and functions to take in consideration when developing a machine learning model with text classification in TensorFlow.js. The training process needs to be performed multiple times as there are many parameters which has an affect on the model results. The model results can be improved by optimising and finding the best combination between different functions and parameters.

Deep Learning is a powerful and rapidly advancing technology that has shown promising results within the field of weather forecasting. Implementing and using deep learning models can however be challenging due to their complexity. One approach to potentially overcome the challenges with deep learning is to run deep learning models directly in the web browser. This approach introduces several advantages, including accessibility, data privacy, and the ability to access device sensors. The ability to run deep learning models on the web browser thus opens new possibilities for research and development in areas such as weather forecasting. In this thesis, two deep learning models that run in the web browser are implemented using JavaScript and TensorFlow.js to predict wind speed in the near future. Specifically, the application of Long Short-Term Memory and Gated Recurrent Units models are investigated. The results demonstrate that both the Long Short-Term Memory and Gated Recurrent Units models achieve similar performance and are able to generate predictions that closely align with the expected patterns when the variations in the data are less significant. The best performing Long Short-Term Memory model achieved a mean squared error of 0.432, a root mean squared error of 0.657 and a mean average error of 0.459. The best performing Gated Recurrent Units model achieved a mean squared error of 0.435, a root mean squared error of 0.660 and a mean average error of 0.461. / Djupinlärning är en kraftfull teknik som genomgår snabb utveckling och har uppnått lovande resultat inom väderprognoser. Att implementera och använda djupinlärningsmodeller kan dock vara utmanande på grund av deras komplexitet. Ett möjligt sätt att möta utmaningarna med djupinlärning är att köra djupinlärningsmodeller direkt i webbläsaren. Detta sätt medför flera fördelar, inklusive tillgänglighet, dataintegritet och möjligheten att använda enhetens egna sensorer. Att kunna köra djupinlärningsmodeller i webbläsaren bidrar därför med möjligheter för forskning och utveckling inom områden såsom väderprognoser. I denna studie implementeras två djupinlärningsmodeller med JavaScript och TensorFlow.js som körs i webbläsaren för att prediktera vindhastighet i en nära framtid. Specifikt undersöks tillämpningen av modellerna Long Short-Term Memory och Gated Recurrent Units. Resultaten visar att både Long Short-Term Memory och Gated Recurrent Units modellerna presterar lika bra och kan generera prediktioner som är nära förväntade mönster när variationen i datat är mindre signifikant. Den Long Short-Term Memory modell som presterade bäst uppnådde en mean squared error på 0.432, en root mean squared error på 0.657 och en mean average error på 0.459. Den Gated Recurrent Units modell som presterade bäst uppnådde en mean squared error på 0.435, en root mean squared error på 0.660 och en mean average error på 0.461.