Essays about: "training optimization"
Showing result 1 - 5 of 146 essays containing the words training optimization.
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1. Evaluation of Deep Q-Learning Applied to City Environment Autonomous Driving
University essay from Uppsala universitet/Signaler och systemAbstract : This project’s goal was to assess both the challenges of implementing the Deep Q-Learning algorithm to create an autonomous car in the CARLA simulator, and the driving performance of the resulting model. An agent was trained to follow waypoints based on two main approaches. READ MORE
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2. Deep reinforcement learning for automated building climate control
University essay from Linköpings universitet/Institutionen för datavetenskapAbstract : The building sector is the single largest contributor to greenhouse gas emissions, making it a natural focal point for reducing energy consumption. More efficient use of energy is also becoming increasingly important for property managers as global energy prices are skyrocketing. READ MORE
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3. Optimising Machine Learning Models for Imbalanced Swedish Text Financial Datasets: A Study on Receipt Classification : Exploring Balancing Methods, Naive Bayes Algorithms, and Performance Tradeoffs
University essay from Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)Abstract : This thesis investigates imbalanced Swedish text financial datasets, specifically receipt classification using machine learning models. The study explores the effectiveness of under-sampling and over-sampling methods for Naive Bayes algorithms, collaborating with Fortnox for a controlled experiment. READ MORE
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4. EMONAS : Evolutionary Multi-objective Neuron Architecture Search of Deep Neural Network
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Customized Deep Neural Network (DNN) accelerators have been increasingly popular in various applications, from autonomous driving and natural language processing to healthcare and finance, etc. However, deploying them directly on embedded system peripherals within real-time operating systems (RTOS) is not easy due to the paradox of the complexity of DNNs and the simplicity of embedded system devices. READ MORE
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5. On Linear Mode Connectivity up to Permutation of Hidden Neurons in Neural Network : When does Weight Averaging work?
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Neural networks trained using gradient-based optimization methods exhibit a surprising phenomenon known as mode connectivity, where two independently trained network weights are not isolated low loss minima in the parameter space. Instead, they can be connected by simple curves along which the loss remains low. READ MORE