Sökning: "Djup maskininlärning"
Visar resultat 1 - 5 av 39 uppsatser innehållade orden Djup maskininlärning.
1. ML implementation for analyzing and estimating product prices
Kandidat-uppsats, Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)Sammanfattning : Efficient price management is crucial for companies with many different products to keep track of, leading to the common practice of price logging. Today, these prices are often adjusted manually, but setting prices manually can be labor-intensive and prone to human error. LÄS MER
2. Real-time adaptation of robotic knees using reinforcement control
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Microprocessor-controlled knees (MPK’s) allow amputees to walk with increasing ease and safety as technology progresses. As an amputee is fitted with a new MPK, the knee’s internal parameters are tuned to the user’s preferred settings in a controlled environment. LÄS MER
3. Building a Deep Neural Network From Scratch
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Machine learning is becoming increasingly common in our society and is predictedto have a major impact in the future. Therefore, it would be both interesting and valuable tohave a deep understanding of one of the most used algorithms in machine learning, deepneural network. LÄS MER
4. Quality Assuring an Image Data Pipeline with Transfer Learning : Using Computer Vision Methodologies
Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen Vi3Sammanfattning : The computer vision field has taken big steps forwards and the amount of models and datasets that are being released is increasing. A large number of contemporary models are the result of extensive training sessions on massive datasets, reflecting a significant investment of time and computational resources. LÄS MER
5. Reusage classification of damaged Paper Cores using Supervised Machine Learning
M1-uppsats, Högskolan i Halmstad/Akademin för informationsteknologiSammanfattning : This paper consists of a project exploring the possibility to assess paper code reusability by measuring chuck damages utilizing a 3D sensor and usingMachine Learning to classify reusage. The paper cores are part of a rolling/unrolling system at a paper mill whereas a chuck is used to slow and eventually stop the revolving paper core, which creates damages that at a certain point is too grave for reuse. LÄS MER