Sökning: "deep learning"
Visar resultat 6 - 10 av 2058 uppsatser innehållade orden deep learning.
6. Evaluation of Deep Q-Learning Applied to City Environment Autonomous Driving
Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Signaler och systemSammanfattning : 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. LÄS MER
7. Optimizing Soak Test Reviews: A Comparative Study of Deep Learning Architectures
Master-uppsats, Lunds universitet/Institutionen för datavetenskapSammanfattning : .... LÄS MER
8. Deep reinforcement learning for automated building climate control
Master-uppsats, Linköpings universitet/Institutionen för datavetenskapSammanfattning : 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. LÄS MER
9. Transforming Chess: Investigating Decoder-Only Architecture for Generating Realistic Game-Like Positions
Master-uppsats, Umeå universitet/Institutionen för datavetenskapSammanfattning : Chess is a deep and intricate game, the master of which depends on learning tens of thousands of the patterns that may occur on the board. At Noctie, their mission is to aid this learning process through humanlike chess AI. A prominent challenge lies in curating instructive chess positions for students. LÄS MER
10. Self-Supervised Learning for Tabular Data: Analysing VIME and introducing Mix Encoder
Kandidat-uppsats, Lunds universitet/Fysiska institutionenSammanfattning : We introduce Mix Encoder, a novel self-supervised learning framework for deep tabular data models based on Mixup [1]. Mix Encoder uses linear interpolations of samples with associated pretext tasks to form useful pre-trained representations. LÄS MER